A model predictive control method based on discrete space vector modulation

Abstract

The present invention discloses a model predictive control method based on discrete space vector modulation, which belongs to the field of power electronics and power transmission. The method comprises: calculating the number of times n+m by the cost function, and discretizing the two-level voltage vector plane space into 2 n+m‑1 The optimal voltage vector search method is to first calculate the phase angle and then the amplitude. The search for phase angle and amplitude requires n and m cost function calculations respectively, n ≥ 2, m ≥ 1. Each iteration compares two candidate voltage vectors and selects the one with the smaller cost function to proceed to the next step. First, the amplitude is fixed to (0.5 + 1 / 2 m+1 )u m , search for the optimal voltage vector phase angle θ opt ; Secondly, the fixed phase angle is θ opt , search for the optimal voltage vector amplitude u opt The output voltage vector generates switching pulse signals for each phase bridge arm, outputting a voltage waveform. Compared with existing methods, this method significantly improves the accuracy of optimal vector search, reduces the harmonic content of the output waveform, and enhances control performance, while maintaining the same number of cost function calculations.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of power electronics and power transmission, and more particularly relates to a model predictive control method based on discrete space vector modulation. BACKGROUND

[0002] The traditional model predictive control (MPC) method calculates the cost function of fixed 8 voltage vectors (VVs), applies the VV with the minimum cost function in the control period, further generates the phase bridge arm switching pulse signal, and outputs the voltage waveform. However, there is a large deviation between the fixed 8 VVs and the optimal VV, which further generates additional harmonic components and deteriorates the control performance.

[0003] Therefore, in order to improve the above-mentioned defects, domestic and foreign scholars have carried out related research and proposed a MPC method based on double vectors and three vectors. However, the MPC method based on multiple vectors faces a large amount of calculation in each control period, and the calculation capacity of the microcomputer is limited, which may cause the sum of the calculation and action time to exceed the control period, and the optimal control cannot be achieved. Further, the MPC method based on discrete space vector modulation is proposed, which discretizes the entire space vector into several points, each point corresponds to a selectable virtual VV, and the virtual VV closest to the optimal VV is found through the traversal method and is used as the applied VV. With the increase of the discrete degree, the error between the applied VV and the optimal VV gradually decreases, but a large amount of calculation is also brought. Therefore, it is necessary to further improve the search accuracy of the optimal VV, reduce the harmonic content, and improve the control performance under the limited calculation amount. SUMMARY

[0004] In view of the defects of the prior art, the purpose of the present application is to provide a model predictive control method based on discrete space vector modulation, which aims to improve the search accuracy of the optimal VV, reduce the harmonic content, and improve the control performance under the limited calculation amount.

[0005] To achieve the above-mentioned purpose, the present application provides a search method of optimal voltage vector, comprising the following steps:

[0006] S1-1, the amplitude of the fixed voltage vector VV is (1-1 / 2 m+1 )u m ; wherein the entire converter output voltage vector space is a circular planar space with the origin as the center, and u dc is the DC bus voltage; according to the calculation number n+m of the cost function, m is the amplitude search number, n is the phase angle search number, n≥2, m≥1, 2 n+m-1 discrete points are generated, corresponding to 2 n+m-1An alternative virtual voltage vector;

[0007] S1-2, determine the included angle difference Δθ = 2π / 2 according to the number of phase angle searches n n-1 ; Wherein, n≥2;

[0008] S1-3, the phase angle selection range is [0, 2π], and the phase angles of the two candidate VVs V1 and V2 are θ(V1) = Δθ / 2 = 2π / 2 n And θ(V2) = 2π-Δθ / 2 = 2π-2π / 2 n ; Calculate the cost function g respectively, and select the candidate VV with smaller cost function as V opt1 ;

[0009] S1-4, if V opt1 is V1, discard V2, add candidate voltage vector V3, the phase angle selection range is [0, π], and the phase angles of the two candidate VVs V1 and V3 are θ(V1) = Δθ / 2 = 2π / 2 n And θ(V3) = π-Δθ / 2 = π-2π / 2 n ; If V opt1 is V2, discard V1, add candidate voltage vector V3, the phase angle selection range is [π, 2π], and the phase angles of the two candidate VVs V2 and V3 are θ(V2) = 2π-Δθ / 2 = 2π-2π / 2 n And θ(V3) = π+Δθ / 2 = π+2π / 2 n ; Calculate the cost function g respectively, and select the candidate VV with smaller cost function as V opt2 ;

[0010] S1-5, according to the rule of S1-4, that is, the phase angles of the compared candidate VVs in each round are the phase angle selection range ±Δθ / 2, and the phase angle selection range of this round of iteration is divided into two halves according to the angle bisector, and the half region with smaller cost function is reserved;

[0011] S1-6, after n-1 rounds of iteration, the phase angle selection range is reduced from the initial 2π to 2π / 2 n-1 , the maximum phase angle error ε θ = π / 2 n-1 , and the optimal phase angle of the candidate voltage vector is θ(V opt(n-1) );

[0012] S2-1, fix the phase angle of the candidate VV V opt(n-1) to θ(V opt(n-1) );

[0013] S2-2, determine the amplitude difference Δu = 1 / 2 m u m of the compared candidate VV according to the amplitude search number m;

[0014] S2-3, the amplitude selection range is [0, u m ], increase the candidate voltage vector V n+1 , two candidate VV V opt(n-1) and V n+1 The amplitudes are u(V opt(n-1) )=u m -1 / 2Δu=(1-1 / 2 m+1 )u m and u(V n+1 )=1 / 2Δu=1 / 2 m+1 u m , calculate the cost function g respectively, select the candidate VV with the smaller cost function and record it as V optn ; Among them, V opt(n-1) The cost function g(V opt(n-1) ) has been calculated and does not need to be repeated, so this step only calculates one cost function g(V n+1 );

[0015] S2-4, if V optn V opt(n-1) , then discard V n+1 , increase the candidate voltage vector V n+2 , the amplitude selection range is [1 / 2u m ,u m ], two candidates VV V opt(n-1) and V n+2 The amplitudes are u(V opt(n-1) )=u m -1 / 2Δu=(1-1 / 2 m+1 )u m and u(V n+2 )=1 / 2u m -1 / 2Δu=(1 / 2-1 / 2 m+1 )u m ; If V optn V n+1 , then discard V opt(n-1) , increase the candidate voltage vector V n+2 , the amplitude selection range is [0, 1 / 2u m ], two candidates VV V n+1 and V n+2 The amplitudes are u(V n+1 )=1 / 2Δu=1 / 2 m+1 u m and u(V n+2 )=1 / 2u m -1 / 2Δu=1 / 2u m -1 / 2 m+1 u m; respectively calculate cost function g, select the candidate VV with smaller cost function g as V optn+2 ;

[0016] S2-5, iteratively calculate according to the rule of S2-4, that is, the amplitude of the candidate VV compared in each round is amplitude selection range ± Δu / 2, the amplitude selection range of this round of iteration is divided into two halves according to the bisector, and the half region with smaller cost function is reserved;

[0017] S2-6, after m rounds of iteration, the amplitude selection range is reduced from the initial u m to 1 / 2 m u m , the maximum amplitude error ε u = 1 / 2 m+1 u m , the optimal amplitude of the candidate voltage vector is u(V opt(n+m-1) ), so as to obtain the optimal voltage vector.

[0018] Further, the cost function g is represented as:

[0019] g = [i 1αref -i 1α (k+1)] 2 +[i 1βref -i 1β (k+1)] 2

[0020] Wherein, k is the current time, k+1 is the next time, i 1αβ =[i 1α ,i 1β ] T is the primary current, i 1αβref =[i 1αref ,i 1βref ] T is the αβ axis primary reference current.

[0021] The application also provides a model predictive control method based on discrete space vector modulation, comprising the following steps:

[0022] (1) the αβ axis reference current is calculated according to the sampled phase current and speed;

[0023] (2) the αβ axis reference current and the sampled αβ axis predicted current are brought into the cost function, the optimal voltage vector is obtained according to the above-mentioned optimal voltage vector searching method, and is applied to the converter at the next time;

[0024] (3) the bridge arm switching pulse signal of each phase is generated according to the optimal voltage vector, and the voltage waveform is output.

[0025] The present invention also provides an electronic device, comprising: a computer-readable storage medium and a processor;

[0026] The computer-readable storage medium is used to store executable instructions;

[0027] The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the above method.

[0028] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the above method.

[0029] The present invention also provides a computer program product, comprising a computer program or instructions, characterized in that the computer program or instructions implement the above method when executed by a processor.

[0030] Compared with the prior art, the above technical solutions proposed by the present invention can achieve the following:

[0031] Beneficial effects:

[0032] (1) This invention proposes a method for discretizing space vectors. is the radius of the circle, and the origin is the center of the circle. This method can discretize the entire space into 2 n+m-1 points, corresponding to 2 n+m-1 A selectable virtual voltage vector, which provides more vector options compared to traditional discrete methods;

[0033] (2) The present invention proposes a search method that searches for phase angle first and amplitude later. During the search iteration process, only one candidate voltage vector is added in each round. By comparing two of them, it is ensured that an accurate voltage vector can be obtained. Compared with the traditional search method, this method can obtain a more accurate voltage vector within a certain limit on the number of cost function calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a linear induction motor model predictive control based on discrete space vector modulation provided by an embodiment of the present invention.

[0035] Figure 2 Schematic diagram of a VV discrete space vector provided by an embodiment of the present invention.

[0036] Figure 3 This is a theoretical analysis of the optimal VV search method provided by an embodiment of the present invention.

[0037] Figure 4 These are the execution steps of the optimal VV search method provided by the embodiment of the present invention.

[0038] Figure 5 is the overall control block diagram of the linear induction motor model predictive current control method based on discrete space vector modulation provided by the embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0040] The present application takes a linear induction motor as an example for detailed description, and provides a linear induction motor model predictive control process based on discrete space vector modulation, as shown in Figure 1 , specifically comprising the following steps:

[0041] According to the equivalent circuit model of the linear induction motor, a prediction model of the primary current and the secondary flux linkage is established.

[0042] Specifically, according to the classical T-type equivalent circuit model proposed by J. Duncan, the expressions of the primary current and the secondary flux linkage in the αβ axis are:

[0043]

[0044] wherein, i 1αβ =[i 1α ,i 1β ] T is the primary current, ψ 2αβ =[ψ 1α ,ψ 1β ] T is the secondary flux linkage, u 1αβ =[u 1α ,u 1β ] T is the primary voltage, L1=L m +L 1σ , L2=L m +L 2σ , ω2=v2π / τ, R1, R2, L1, L2, ω2, v2 and τ respectively represent the primary resistance, the secondary resistance, the primary inductance, the secondary inductance, the secondary angular frequency, the speed and the pole pitch.

[0045] Based on formula (1), the prediction model of the primary current and the secondary flux linkage can be represented as:

[0046]

[0047] Wherein, T s is a control period, (k) is a current time, and (k+1) is a next time.

[0048] According to the sampling of the motor phase current and the speed signal by the current sensor and the speed sensor, the alpha-beta axis primary reference current is further calculated.

[0049] Specifically, the q-axis primary reference current is obtained by subtracting the given speed from the sampling speed and then performing PI proportion integration; the d-axis primary reference current is obtained by subtracting the given secondary flux amplitude from the observed secondary flux amplitude after the sampling speed and the sampling current are observed to obtain the secondary flux amplitude and the phase angle and then performing PI proportion integration; the alpha-beta axis primary reference current is further transformed from the dq axis primary reference current through Park transformation.

[0050] The voltage vector with the minimum cost function is selected as the optimal voltage vector, and is applied to the converter at the next time.

[0051] Specifically, after the predicted current and the reference current are obtained, the sum of squares of the difference between the primary current reference value and the predicted value is defined as the cost function g.

[0052] g=[i 1αref -i 1α (k+1)] 2 +[i 1βref -i 1β (k+1)] 2 (3)

[0053] In the traditional two-level model predictive current control strategy, u 1αβ Only 8 VVs are available for selection, and the error between the actual optimal VV and the selected VV is large, so that the current harmonic content is large, additional thrust fluctuation is generated, and the control performance is affected.

[0054] To increase the number of available VVs, the model predictive current control is improved, and a two-level discrete space vector method is designed, as shown in the figure. Figure 2 Wherein, n is the cost function calculation number when searching the phase angle, and m is the cost function calculation number when searching the amplitude.

[0055] When n=2 and m=1, the entire space is discretized into 4 points, the maximum phase angle error is π, and the maximum amplitude error is 1 / 2u m .

[0056] When n=3 and m=2, the entire space is discretized into 16 points, the maximum phase angle error is π / 2, and the maximum amplitude error is 1 / 4u m .

[0057] When n = 4, m = 3, the whole space is discretized into 64 points, the maximum phase error is π / 4, and the maximum amplitude error is 1 / 8u m .

[0058] …

[0059] According to n and m, the whole space can be discretized into 2 n+m-1 points, corresponding to 2 n+m-1 selectable virtual voltage vectors (n≥2, m≥1), the maximum phase error is ε θ = π / 2 n-1 , and the maximum amplitude error is ε u = 1 / 2 m+1 u m .

[0060] If all virtual VVs are traversed to find the optimal VV, the calculation amount is very huge. Therefore, an optimal voltage vector searching method is designed. Figure 3 The principle of searching the optimal VV in the discretized space vector plane is described.

[0061] Figure 3 In the formula, V opt is the optimal VV. If a search order of amplitude first and phase second is adopted, first, the amplitude |OB| is found as the minimum amplitude with the phase angle fixed as 0; second, V OD is found as the minimum VV with the amplitude fixed as |OB|. Obviously, there is an error ε = V DA between V OD and V opt , which is 0 only when the assumed phase angle when searching the amplitude is the same as the phase angle of V opt , which is almost impossible.

[0062] On the contrary, if a search order of phase first and amplitude second is adopted, first, the phase θ OE is found as the minimum phase angle with the amplitude fixed as u m ; second, u OF is found as the minimum VV with the phase fixed as θ OE . In theory, when there are enough virtual VVs, the error ε = u FA can approach 0. Therefore, searching the phase first and then searching the amplitude is a way to find the optimal VV.

[0063] Figure 4 The execution steps of the optimal VV searching method when n = 4, m = 3 are given, and the optimal VV is assumed to be V opt . The specific steps are as follows:

[0064] 1. Optimal VV phase angle search, fixing the VV amplitude as 15 / 16um , Δθ = π / 4;

[0065] S1, the phase angle selection range is [0, 2π], the phase angles of two candidate VVs are θ(V1) = π / 8 and θ(V2) = -π / 8 respectively, the cost functions g(V1) and g(V2) are calculated respectively, and the candidate VV with smaller cost function is recorded as V1 = V opt1 ;

[0066] S2, the phase angle selection range is [0, π], the phase angles of two candidate VVs are θ(V1) = π / 8 and θ(V3) = π - π / 8 respectively, the cost function g(V3) is calculated, and the candidate VV with smaller cost function is recorded as V1 = V opt2 ;

[0067] S3, the phase angle selection range is [0, π / 2], the phase angles of two candidate VVs are θ(V1) = π / 8 and θ(V4) = π / 2 - π / 8 respectively, the cost function g(V4) is calculated, and the candidate VV with smaller cost function is recorded as V4 = V opt3 ;

[0068] 2, the amplitude search of the optimal VV, the phase angle of the VV is fixed as θ(V opt3 ), Δu = 1 / 8u m ;

[0069] S4, the amplitude selection range is [0, u m ], the amplitudes of two candidate VVs are u(V opt3 ) = 15 / 16u m and u(V5) = 1 / 16u m , the cost function g(V5) is calculated, and the candidate VV with smaller cost function is recorded as V4 = V opt4 .

[0070] S5, the amplitude selection range is [1 / 2u m , u m ], the amplitudes of two candidate VVs are u(V opt4 ) = 15 / 16u m and u(V6) = 9 / 16u m , the cost function g(V6) is calculated, and the candidate VV with smaller cost function is recorded as V6 = V opt5 .

[0071] S6, the amplitude selection range is [1 / 2u m , 3 / 4u m ], the amplitudes of two candidate VVs are u(V opt5 ) = 9 / 16u m and u(V7) = 11 / 16u m, calculate cost function g(V7), select the candidate VV with smaller cost function as V7=V opt6 .

[0072] According to the prediction model and the cost function, an optimal VV searching method is designed, the number of times of cost function calculation is n+m after n+m-1 times of iterative searching, 2 n+m-1 optimal VV candidates are generated, and the selected V optn+m-1 is very close to V opt Compared with the method of searching the optimal VV by traversing, the calculation amount is greatly reduced.

[0073] Finally, the optimal VV obtained is sent to the PWM modulator to generate the corresponding PWM signal.

[0074] The overall control block diagram of the model prediction control method based on the discrete space vector modulation provided by the application is shown in Figure 5 .

[0075] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the application, and is not used to limit the application, and any modification, equivalent replacement and improvement made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A method for searching an optimal voltage vector, characterized in that: The following steps are involved: S1-1, fixed voltage vector amplitude is (1-1 / 2 m+1 )u m ; Where m is the number of amplitude searches, m≥1, u m is the radius of the circular plane space of the converter output voltage vector; S1-2, determine the angle difference Δθ=2π / 2 according to the number of phase angle searches n n-1 ; where n≥2; S1-3, the phase angle selection range is [0, 2π], and the phase angles of the two candidate voltage vectors V1 and V2 are θ(V1) = Δθ / 2 = 2π / 2 n and θ(V2)=2π-Δθ / 2=2π-2π / 2 n ; Calculate the cost function g respectively, select the candidate voltage vector with the smaller cost function and record it as V opt1 ; S1-4, if V opt1 If V1 is V2, V2 is discarded and candidate voltage vector V3 is added. The phase angle selection range is [0, π]. The phase angles of the two candidate voltage vectors V1 and V3 are θ(V1) = Δθ / 2 = 2π / 2. n and θ(V3)=π-Δθ / 2=π-2π / 2 n ; If V opt1 If V2 is V1, V1 is discarded and candidate voltage vector V3 is added. The phase angle selection range is [π, 2π]. The phase angles of the two candidate voltage vectors V2 and V3 are θ(V2) = 2π - Δθ / 2 = 2π - 2π / 2. n and θ(V3)=π+Δθ / 2=π+2π / 2 n ; Calculate the cost function g respectively, select the candidate voltage vector with the smaller cost function and record it as V opt2 ; S1-5. Iterate and calculate according to the rule of S1-4. That is, the candidate voltage vector phase angle for each round of comparison is within the phase angle selection range ±Δθ / 2. The phase angle selection range of this iteration is divided into two halves according to the angle bisector, and the half area with the smaller cost function is retained; S1-6, after n-1 rounds of iteration, the phase angle selection range is reduced from the initial 2π to 2π / 2 n-1 , maximum phase angle error ε θ =π / 2 n-1 , the optimal phase angle of the candidate voltage vector is θ(V opt(n-1) ); S2-1. Fixed candidate voltage vector V opt(n-1) The phase angle is θ(V opt(n-1) ); S2-2. Determine the amplitude difference Δu=1 / 2 of the candidate voltage vector according to the number of amplitude searches m m u m ; S2-3, the amplitude selection range is [0, u m ], increase the candidate voltage vector V n+1 , two candidate voltage vectors V opt(n-1) and V n+1 The amplitudes are u(V opt(n-1) )=u m -1 / 2Δu=(1-1 / 2 m+1 )u m and u(V n+1 )=1 / 2Δu=1 / 2 m+1 u m , calculate the cost function g respectively, select the candidate voltage vector with the smaller cost function and record it as V optn ; S2-4, if V optn V opt(n-1) , then discard V n+1 , increase the candidate voltage vector V n+2 , the amplitude selection range is [1 / 2u m ,u m ], two candidate voltage vectors V opt(n-1) and V n+2 The amplitudes are u(V opt(n-1) )=u m -1 / 2Δu=(1-1 / 2 m+1 )u m and u(V n+2 )=1 / 2u m -1 / 2Δu=(1 / 2-1 / 2 m+1 )u m ; If V optn V n+1 , then discard V opt(n-1) , increase the candidate voltage vector V n+2 , the amplitude selection range is [0, 1 / 2u m ], two candidate voltage vectors V n+1 and V n+2 The amplitudes are u(V n+1 )=1 / 2Δu=1 / 2 m+1 u m and u(V n+2 )=1 / 2u m -1 / 2Δu=1 / 2u m -1 / 2 m+1 u m ; Calculate the cost function g respectively, select the candidate voltage vector with the smaller cost function and record it as V optn+2 ; S2-5. Iterate and calculate according to the rule of S2-4. That is, the amplitude of the candidate voltage vector for each round of comparison is the amplitude selection range ±Δu / 2. The amplitude selection range of this round of iteration is divided into two halves according to the bisector, and the half area with the smaller cost function is retained; S2-6, after m rounds of iteration, the amplitude selection range is changed from the initial u m Reduce to 1 / 2 m u m , the maximum amplitude error ε u =1 / 2 m+ 1 u m , the optimal amplitude of the candidate voltage vector is u(V opt(n+m-1) ), thereby obtaining the optimal voltage vector.

2. The method for searching the optimal voltage vector according to claim 1, characterized in that: The cost function g is expressed as: g=[i 1αref -i 1α (k+1)] 2 +[i 1βref -i 1β (k+1)] 2 Among them, k is the current moment, k+1 is the next moment, i 1αβ =[i 1α ,i 1β ] T is the primary current, i 1αβref =[i 1αref ,i 1βref ] T is the primary reference current of αβ axis.

3. The method for searching the optimal voltage vector according to claim 1, characterized in that: In step S2-3, V opt(n-1) The cost function g(V opt(n-1) ) is equal to the cost function g(V opt(n-1) ).

4. A model predictive control method based on discrete space vector modulation, characterized in that: The following steps are involved: (1) Calculate the αβ axis reference current based on the sampled phase current and speed; (2) Substituting the αβ-axis reference current and the sampled αβ-axis predicted current into the cost function, obtaining the optimal voltage vector according to the optimal voltage vector search method according to any one of claims 1 to 3, and applying it to the converter at the next moment; (3) Generate the bridge arm switch pulse signal of each phase according to the optimal voltage vector and output the voltage waveform.

5. An electronic device, characterized in that: include: Computer-readable storage media and processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the method according to claim 4.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the method according to claim 4.

7. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to claim 4 is implemented.

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