Model-free predictive current control method and device for energy storage converter and medium

Abstract

The invention relates to the technical field of converter control, and discloses a model-free predictive current control method and device for an energy storage converter and a medium. According to the method, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the sector where the corresponding reference voltage is located can be obtained, compared with a traditional symmetric double-vector scheme, the generalized double vectors are introduced, the better steady-state effect under the high modulation ratio can be obtained, and the harmonic content is greatly reduced. Finally, on the basis of considering a preset objective function of frequency control, a driving signal for each switching tube for controlling the current of the energy storage converter can be generated in combination with the first voltage candidate vector sequence and the second voltage candidate vector sequence. Therefore, by implementing the method, the steady-state performance of the model-free predictive control of the energy storage converter is effectively improved by combining the improved generalized symmetric double-vector modulation method with the model-free predictive control.

Description

Technical Field

[0001] The present invention relates to the technical field of converter control, and in particular to a model-free predictive current control method, device and medium for an energy storage converter. Background Art

[0002] Energy storage converters are widely used in industrial fields such as high-voltage direct current transmission and renewable energy generation. Model predictive control based on space vector pulse width modulation is a common control method applied to energy storage converters. This method can achieve fast dynamic response performance, low steady-state current harmonics and fixed switching frequency. However, when the system parameters are disturbed, the control performance of the model-based predictive control method will also be affected.

[0003] When the traditional symmetrical dual-vector model predicts current control, the vector sequence used is a combination of a zero vector and a non-zero vector, which cannot track the reference voltage more accurately, so the steady-state performance is poor under high modulation ratios. Furthermore, the existing technology still requires six traversals to select the optimal non-zero vector by minimizing the objective function, which is cumbersome to calculate. Summary of the invention

[0004] In view of this, the present invention provides a model-free predictive current control method, device and medium for an energy storage converter to solve the problem that the adopted vector sequence is a combination of a zero vector and a non-zero vector, which cannot track the reference voltage more accurately, so the steady-state performance is poor under a high modulation ratio. Furthermore, the prior art still needs to traverse six times to select the optimal non-zero vector by minimizing the objective function, which is cumbersome in calculation.

[0005] In a first aspect, the present invention provides a model-free predictive current control method for an energy storage converter, the method comprising:

[0006] Acquire multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals on the AC side of the energy storage converter within multiple historical control cycles; construct an improved target super-local model based on the multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals; obtain a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located based on the multiple target grid current signals, preset active power and preset reactive power through the target super-local model and the improved generalized symmetric dual-vector modulation method; based on a preset objective function, generate a drive signal for each switch tube for controlling the current of the energy storage converter using the first voltage candidate vector sequence and the second voltage candidate vector sequence, and the preset objective function is used to characterize the optimization target taking frequency control into account.

[0007] The energy storage converter model-free predictive current control method provided by the present invention can construct a corresponding improved target super-local model by combining multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals on the AC side of the energy storage converter in multiple historical control cycles. Secondly, by combining the target super-local model and the improved generalized symmetric dual-vector modulation method, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the corresponding reference voltage sector can be obtained. Compared with the traditional symmetric dual-vector scheme, by introducing the generalized dual vector, a better steady-state effect can be obtained under a high modulation ratio, and the harmonic content is greatly reduced. Finally, on the basis of considering the preset objective function of frequency control, the first voltage candidate vector sequence and the second voltage candidate vector sequence can be combined to generate a drive signal for each switch tube for controlling the current of the energy storage converter. By introducing the candidate vector sequence and adding frequency control to the preset objective function, the switching frequency can be effectively controlled. Therefore, by implementing the present invention, by combining the improved generalized symmetric dual-vector modulation method with model-free predictive control, the steady-state performance of the model-free predictive control of the energy storage converter is effectively improved.

[0008] In an optional implementation, based on a plurality of target grid voltage signals, a plurality of target grid current signals and a plurality of target output voltage signals, an improved target hyperlocal model is constructed, including:

[0009] An improved initial hyperlocal model is obtained; a plurality of grid current differences within a plurality of historical control cycles are calculated based on a plurality of target grid current signals; a model parameter set of the initial hyperlocal model is calculated based on a plurality of target grid voltage signals, a plurality of target output voltage signals and a plurality of grid current differences after being processed by the initial hyperlocal model; a target hyperlocal model is determined based on the initial hyperlocal model and the model parameter set.

[0010] The model-free predictive current control method for the energy storage converter provided by the present invention can calculate multiple grid current differences within multiple historical control cycles through multiple target grid current signals, and then calculate multiple model parameters of the improved initial super-local model. Finally, the obtained multiple model parameters are input into the initial super-local model to obtain a target super-local model with known parameters, which provides support for the subsequent determination of two voltage candidate vector sequences.

[0011] In an optional implementation, based on multiple target grid current signals, preset active power and preset reactive power, a first voltage candidate vector sequence and a second voltage candidate vector sequence of a sector where a reference voltage is located are obtained through processing by a target super-local model and an improved generalized symmetric dual-vector modulation method, including:

[0012] Based on the preset active power and the preset reactive power, a grid current reference value is calculated; based on multiple target grid current signals and grid current reference values, multiple converter side reference voltage values ​​are calculated after being processed by a target super-local model; based on multiple converter side reference voltage values, a first voltage candidate vector sequence and a second voltage candidate vector sequence of a sector where the reference voltage is located are obtained after being processed by an improved generalized symmetric dual-vector modulation method.

[0013] The model-free predictive current control method for the energy storage converter provided by the present invention can calculate multiple converter-side reference voltage values ​​through target super-local model processing. Further, in combination with the improved generalized symmetric dual-vector modulation method, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the sector where the reference voltage is located can be calculated. Therefore, by implementing the present invention, by introducing the generalized dual vector, a better steady-state effect can be obtained under a high modulation ratio, and the harmonic content is greatly reduced. Furthermore, by combining the improved generalized symmetric dual-vector modulation method for model-free predictive control, the steady-state performance of the model-free predictive control of the energy storage converter can be effectively improved.

[0014] In an optional implementation, based on multiple converter-side reference voltage values, a first voltage candidate vector sequence and a second voltage candidate vector sequence of a sector where the reference voltage is located are obtained by processing with an improved generalized symmetric dual-vector modulation method, including:

[0015] The first non-zero basic vector action time value, the second non-zero basic vector action time value and the zero vector action time value of the sector where the reference voltage is located are calculated using the deadbeat principle; based on the first non-zero basic vector action time value, the second non-zero basic vector action time value and the zero vector action time value, a first voltage candidate vector sequence and a second voltage candidate vector sequence are determined.

[0016] In an optional implementation, based on a preset objective function, using a first voltage candidate vector sequence and a second voltage candidate vector sequence, generating a drive signal for each switch tube of the energy storage converter includes:

[0017] Calculate a first duty cycle of a first voltage candidate vector sequence and a second duty cycle of a second voltage candidate vector sequence; determine a target voltage candidate vector sequence in the first voltage candidate vector sequence and the second voltage candidate vector sequence based on a preset objective function; determine a target duty cycle of the target voltage candidate vector sequence based on the first duty cycle and the second duty cycle; and generate a drive signal for each switch tube for controlling the current of the energy storage converter based on the target voltage candidate vector sequence and the target duty cycle.

[0018] The model-free predictive current control method for an energy storage converter provided by the present invention determines a target voltage candidate vector sequence from two obtained voltage candidate vector sequences by presetting an objective function, and combines the target duty cycle of the target voltage candidate vector sequence to generate a drive signal for each switching tube for controlling the current of the energy storage converter, thereby effectively improving the steady-state performance of the model-free predictive control of the energy storage converter.

[0019] In an optional implementation, based on a preset objective function, determining a target voltage candidate vector sequence in the first voltage candidate vector sequence and the second voltage candidate vector sequence includes:

[0020] Based on a preset objective function, a first objective function value of the first voltage candidate vector sequence and a second objective function value of the second voltage candidate vector sequence are calculated; based on the first objective function value and the second objective function value, a target voltage candidate vector sequence is determined in the first voltage candidate vector sequence and the second voltage candidate vector sequence.

[0021] In a second aspect, the present invention provides a model-free predictive current control device for an energy storage converter, the device comprising:

[0022] An acquisition module is used to acquire multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals on the AC side of the energy storage converter within multiple historical control cycles; a construction module is used to construct an improved target super-local model based on multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals; a processing module is used to obtain a first voltage candidate vector sequence and a second voltage candidate vector sequence for a sector where a reference voltage is located based on multiple target grid current signals, preset active power and preset reactive power, through processing by the target super-local model and an improved generalized symmetric dual-vector modulation method; a generation module is used to generate a drive signal for each switch tube for controlling the current of the energy storage converter based on a preset objective function and using the first voltage candidate vector sequence and the second voltage candidate vector sequence, wherein the preset objective function is used to characterize an optimization target that takes frequency control into consideration.

[0023] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the model-free predictive current control method for an energy storage converter according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the model-free predictive current control method for an energy storage converter according to the first aspect or any corresponding embodiment thereof.

[0025] In a fifth aspect, the present invention provides a computer program product, including computer instructions, which are used to enable a computer to execute the model-free predictive current control method for an energy storage converter according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a flow chart of a model-free predictive current control method for an energy storage converter according to an embodiment of the present invention;

[0028] Figure 2 is a flow chart of another model-free predictive current control method for an energy storage converter according to an embodiment of the present invention;

[0029] Figure 3 is a flow chart of another model-free predictive current control method for an energy storage converter according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of different currents corresponding to a conventional symmetrical dual-vector model predictive current control method according to an embodiment of the present invention;

[0031] Figure 5 Schematic diagrams of different currents corresponding to a model-free predictive current control method for an energy storage converter based on an improved generalized symmetric dual-vector according to an embodiment of the present invention;

[0032] Figure 6 is a current THD analysis diagram of a conventional symmetrical dual-vector model predictive current control method under high modulation ratio conditions when the sampling rate is 10 kHz according to an embodiment of the present invention;

[0033] Figure 7 It is a current THD analysis diagram at a sampling rate of 10kHz under high modulation ratio conditions of a model-free predictive current control method for an energy storage converter based on an improved generalized symmetric dual-vector according to an embodiment of the present invention;

[0034] Figure 8 is a current THD comparison bar graph of a conventional symmetrical dual-vector model predictive current control method and an energy storage converter model-free predictive current control method based on an improved generalized symmetrical dual-vector at different operating points according to an embodiment of the present invention at the same switching frequency;

[0035] Fig. 9 is a structural block diagram of a model-free predictive current control device for an energy storage converter according to an embodiment of the present invention;

[0036] Fig.10 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0038] Energy storage converters are widely used in industrial fields such as high-voltage direct current transmission and renewable energy generation. Model predictive control based on space vector pulse width modulation is a common control method applied to energy storage converters. This method can achieve fast dynamic response performance, low steady-state current harmonics and fixed switching frequency. However, when the system parameters are disturbed, the control performance of the model-based predictive control method will also be affected.

[0039] To this end, many scholars have proposed model-free control methods with strong robustness. For example, model-free control is achieved based on current differential, but the steady-state current harmonics are large. Alternatively, the duration of the active vector is obtained by the principle of zero beat, which requires six calculations, and the calculation is cumbersome. Alternatively, by comparing the torque ripple and current harmonics generated by six symmetrical pulse wave generation methods, it is finally found that the wave generation method has the best steady-state performance and the lowest switching frequency. However, the calculation method of the flux variance of this method is not suitable for dual-vector model predictive control, and the steady-state performance is still poor at high modulation ratios.

[0040] According to an embodiment of the present invention, an embodiment of a model-free predictive current control method for an energy storage converter is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] In this embodiment, a model-free predictive current control method for an energy storage converter is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 is a flow chart of a model-free predictive current control method for an energy storage converter according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0042] Step S101, acquiring multiple target grid voltage signals, multiple target grid current signals, and multiple target output voltage signals of the AC side of the energy storage converter within multiple historical control cycles.

[0043] Specifically, by subjecting the initial grid voltage, grid current and sampling values ​​of the converter AC side output voltage to a 2 / 3 transformation, the target grid voltage signal, target grid current signal and target output voltage signal of the AC side of the converter can be obtained in a two-phase stationary coordinate system.

[0044] Furthermore, in this embodiment, taking the past two control cycles as an example, the grid voltage signal e of the past two control cycles can be obtained through the above processing process. k-1 、e k-2 , grid current signal i k-1 、i k-2 and the AC side output voltage signal v of the converter k-1 、v k-2 .

[0045] Step S102 : constructing an improved target hyperlocal model based on a plurality of target grid voltage signals, a plurality of target grid current signals and a plurality of target output voltage signals.

[0046] Among them, the hyperlocal model is a model-free control method, that is, it does not need to know the specific parameter information of the controlled object, and only builds the model based on the input and output data of the system, thereby improving the robustness of the control strategy.

[0047] Specifically, the hyperlocal model can be improved by combining the obtained multiple target grid voltage signals, multiple target grid current signals, and multiple target output voltage signals, and an improved target hyperlocal model can be obtained.

[0048] Step S103, based on multiple target grid current signals, preset active power and preset reactive power, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located are obtained through processing by a target super-local model and an improved generalized symmetric dual-vector modulation method.

[0049] Among them, the generalized symmetrical dual-vector modulation method is a technology used in power systems. It is based on the principle of the symmetrical component method and aims to improve and expand the traditional symmetrical component method by introducing the concept of dual vectors to adapt to a wider range of application scenarios: by acting on two voltage vectors (a non-zero voltage vector and a zero voltage vector) within one control cycle, the steady-state performance of the system can be effectively improved.

[0050] Specifically, in this embodiment, an improved generalized symmetric dual-vector modulation method is used to select and determine the first voltage candidate vector sequence and the second voltage candidate vector sequence of the corresponding reference voltage sector. Compared with the traditional symmetric dual-vector scheme, by introducing the generalized dual-vector, a better steady-state effect can be obtained under a high modulation ratio, and the harmonic content is greatly reduced.

[0051] Step S104: Based on a preset objective function, using the first voltage candidate vector sequence and the second voltage candidate vector sequence, a driving signal for each switch tube for controlling the current of the energy storage converter is generated.

[0052] Specifically, the preset objective function is used to characterize the optimization objective taking frequency control into consideration, that is, frequency control is added to the preset objective function, so that the switching frequency can be effectively controlled.

[0053] Furthermore, the first voltage candidate vector sequence and the second voltage candidate vector sequence obtained in combination can generate a driving signal for each switch tube for controlling the current of the energy storage converter.

[0054] The energy storage converter model-free predictive current control method provided in this embodiment can construct a corresponding improved target super-local model by combining multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals on the AC side of the energy storage converter in multiple historical control cycles. Secondly, by combining the target super-local model and the improved generalized symmetric dual-vector modulation method, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the corresponding reference voltage sector can be obtained. Compared with the traditional symmetric dual-vector scheme, by introducing the generalized dual vector, a better steady-state effect can be obtained under a high modulation ratio, and the harmonic content is greatly reduced. Finally, on the basis of considering the preset objective function of frequency control, the first voltage candidate vector sequence and the second voltage candidate vector sequence can be combined to generate a drive signal for each switch tube for controlling the current of the energy storage converter. By introducing the candidate vector sequence and adding frequency control to the preset objective function, the switching frequency can be effectively controlled. Therefore, by implementing the present invention, by combining the improved generalized symmetric dual-vector modulation method with model-free predictive control, the steady-state performance of the model-free predictive control of the energy storage converter is effectively improved.

[0055] In this embodiment, a model-free predictive current control method for an energy storage converter is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 2 is a flow chart of a model-free predictive current control method for an energy storage converter according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0056] Step S201, obtaining multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals of the AC side of the energy storage converter in multiple historical control cycles. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0057] Step S202 : constructing an improved target hyperlocal model based on a plurality of target grid voltage signals, a plurality of target grid current signals and a plurality of target output voltage signals.

[0058] Specifically, the above step S202 includes:

[0059] Step S2021, obtaining an improved initial hyperlocal model.

[0060] Specifically, the improved initial super-local model is shown in the following equation (1):

[0061]

[0062] Where: F represents the dynamic and total disturbance of the energy storage system; α Indicates input gain.

[0063] Step S2022: Calculate multiple grid current differences within multiple historical control cycles based on multiple target grid current signals.

[0064] Specifically, the grid current difference Δi is calculated using the following equations (2) and (3) respectively: k and Δi k-1 :

[0065] Δi k =i k -i k-1 (2)

[0066] Δi k-1 =i k-1 -i k-2 (3)

[0067] Among them, i k 、i k-1 、i k-2 They respectively represent the target grid current signals at different times.

[0068] Step S2023, based on the multiple target grid voltage signals, the multiple target output voltage signals and the multiple grid current differences, after being processed by the initial hyperlocal model, a model parameter set of the initial hyperlocal model is calculated.

[0069] Specifically, according to the improved initial super-local model shown in the above relationship (1), the current difference expression of the past two control cycles can be obtained, which are respectively expressed as the following relationships (4) and (5):

[0070] Δi k =T SC (F k-1 +α(V k-1 -e k-1 )) (4)

[0071] Δi k-1 =T SC (F k-2 +α(v k-2 -e k-2 )) (5)

[0072] Among them, T SC Indicates the control period.

[0073] Furthermore, by combining multiple target grid voltage signals and multiple target output voltage signals, the grid current difference Δi calculated by the above equations (2) and (3) is k and Δi k-1 Substituting into the current difference expressions shown in the above relations (4) and (5), the model parameters F and α , which can be expressed as the following equations (6) and (7) respectively:

[0074]

[0075]

[0076] When the sampling frequency is high enough, such as greater than 10kHz, it can be considered constant in several consecutive control cycles, that is, F k - 2 =F k - 1 =F k =F.

[0077] Step S2024: determining a target hyperlocal model based on the initial hyperlocal model and the model parameter set.

[0078] Specifically, the calculated model parameters F and α are input into the initial hyperlocal model shown in the above relational expression (1) to obtain a target hyperlocal model with known parameters.

[0079] Step S203, based on multiple target grid current signals, preset active power and preset reactive power, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located are obtained through processing by a target super-local model and an improved generalized symmetric dual-vector modulation method.

[0080] Specifically, the above step S203 includes:

[0081] Step S2031, calculating a grid current reference value based on a preset active power and a preset reactive power.

[0082] Specifically, the grid current reference value i can be calculated using the following relationship (8): ref :

[0083]

[0084] Among them, S ref The given apparent power reference value can be calculated based on the preset active power and the preset reactive power, as shown in the following equation (9):

[0085] S ref =P ref +jQ ref (9)

[0086] Wherein: j represents the imaginary unit.

[0087] Step S2032, based on multiple target grid current signals and grid current reference values, multiple converter side reference voltage values ​​are calculated after being processed by a target super-local model.

[0088] Specifically, according to the obtained target super-local model, the differential expressions of the grid current at time k+1 and time k+2 are shown in the following equations (10) and (11), respectively:

[0089] Δi k+1 =T SC (F k +α(v k -e k )) (10)

[0090] Δi k+2 =T SC (F k+1 +α(v k+1 -e k+1 )) (11)

[0091] Furthermore, the reference voltage value of the converter side can be calculated as shown in the following equation (12):

[0092]

[0093] Among them, i k+2 Represents the grid current reference value at time k+2.

[0094] Step S2033: Based on multiple converter-side reference voltage values, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located are obtained by processing with an improved generalized symmetric dual-vector modulation method.

[0095] Specifically, according to the obtained multiple converter-side reference voltage values, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located can be obtained by synthesizing them through an improved generalized symmetric dual-vector modulation method.

[0096] In some optional implementations, the above step S2033 includes:

[0097] Step a1, using the deadbeat principle to calculate the first non-zero basic vector action time value, the second non-zero basic vector action time value and the zero vector action time value of the sector where the reference voltage is located.

[0098] Step a2: determining a first voltage candidate vector sequence and a second voltage candidate vector sequence based on the first non-zero basic vector action time value, the second non-zero basic vector action time value and the zero vector action time value.

[0099] Specifically, the action times of the two non-zero basic vectors and the zero vector corresponding to the sector where the reference voltage is located are calculated according to the deadbeat principle, and are recorded as: the first non-zero basic vector action time value t1, the second non-zero basic vector action time value t2 and the zero vector action time value t0 respectively:

[0100]

[0101] in, represents the modulation coefficient; θ represents the reference voltage v ref angle.

[0102] Further, the vectors corresponding to the largest two values ​​among t1, t2, and t0 are taken as the optimal vector u x 、u y , the smallest value will be divided equally between the two largest values ​​to serve as the final duty cycle t of the optimal vector x ,t y , and then we can get two candidate vector sequences u x ,u y ,u x and u y ,u x ,u y .

[0103] Step S204: Based on the preset objective function, the first voltage candidate vector sequence and the second voltage candidate vector sequence are used to generate a driving signal for each switch tube for controlling the current of the energy storage converter. Figure 1Step S104 of the illustrated embodiment will not be described in detail here.

[0104] The model-free predictive current control method for energy storage converters provided in this embodiment can calculate multiple grid current differences within multiple historical control cycles through multiple target grid current signals, and then calculate multiple model parameters of the improved initial super-local model, and finally input the obtained multiple model parameters into the initial super-local model to obtain the target super-local model with known parameters. Further, multiple converter-side reference voltage values ​​can be calculated through target super-local model processing, and further, combined with the improved generalized symmetric dual-vector modulation method, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the sector where the reference voltage is located can be calculated. Compared with the traditional symmetric dual-vector scheme, by introducing the generalized dual vector, a better steady-state effect can be obtained under a high modulation ratio, and the harmonic content is greatly reduced. Finally, on the basis of considering the preset objective function of frequency control, the first voltage candidate vector sequence and the second voltage candidate vector sequence can be combined to generate a drive signal for each switch tube used to control the current of the energy storage converter. By introducing the candidate vector sequence and adding frequency control to the preset objective function, the switching frequency can be effectively controlled. Therefore, by implementing the present invention, the steady-state performance of the model-free predictive control of the energy storage converter is effectively improved by combining the improved generalized symmetric dual-vector modulation method with the model-free predictive control.

[0105] In this embodiment, a model-free predictive current control method for an energy storage converter is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 3 is a flow chart of a model-free predictive current control method for an energy storage converter according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0106] Step S301, obtaining multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals of the AC side of the energy storage converter in multiple historical control cycles. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0107] Step S302: construct an improved target hyperlocal model based on multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0108] Step S303: Based on multiple target grid current signals, preset active power and preset reactive power, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located are obtained through processing by the target super-local model and the improved generalized symmetric dual-vector modulation method. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0109] Step S304: Based on a preset objective function, using the first voltage candidate vector sequence and the second voltage candidate vector sequence, generate a drive signal for each switch tube for controlling the current of the energy storage converter.

[0110] Specifically, the above step S304 includes:

[0111] Step S3041 , calculating a first duty cycle of a first voltage candidate vector sequence and a second duty cycle of a second voltage candidate vector sequence.

[0112] Specifically, take the first candidate vector sequence u x ,u y ,u x For example, when calculating the duty cycle of each vector in the three-stage sequence, the proportional coefficient K is introduced to form the actual duty cycle of each segment of the three-stage vector sequence (K×tx, ty, 1-Ktx, according to the following relationship (14) and (15) to calculate the square of the effective value of the current harmonic Ihrms2: It can be obtained that the expression for the square of the effective value of the current harmonic is a quadratic equation about K, and the opening is upward, as shown in the following relationship (16):

[0113]

[0114]

[0115]

[0116] For the convenience of expression, t x Replace with t1, t y Replace with t2.

[0117] Furthermore, e0 represents the current error at time k+1; e3 represents the current error at time k+2; k1 represents the slope of the current error when the first voltage vector of the three-segment vector sequence from time k+1 to time k+2 acts; k2 represents the slope of the current error when the second voltage vector acts.

[0118] Furthermore, when the above relation (16) takes a minimum value, the corresponding horizontal coordinate is obtained, and the optimal K values ​​of the two candidate vector sequences can be obtained, respectively, as shown in the following relations (17) and (18):

[0119]

[0120]

[0121] Among them, Kopt1 represents the K value of the first candidate vector sequence; K opt2 Represents the K value of the second candidate vector sequence.

[0122] Furthermore, optimal duty cycles of two candidate vector sequences, namely, a first duty cycle and a second duty cycle, may be obtained.

[0123] Step S3042: determining a target voltage candidate vector sequence from the first voltage candidate vector sequence and the second voltage candidate vector sequence based on a preset objective function.

[0124] The preset objective function is shown in the following equation (19):

[0125]

[0126] Where: represents the switching frequency, where N represents T SC The total number of internal switch actions.

[0127] In some optional implementations, the above step S3041 includes:

[0128] Step b1, based on a preset objective function, calculating a first objective function value of a first voltage candidate vector sequence and a second objective function value of a second voltage candidate vector sequence.

[0129] Step b2: determining a target voltage candidate vector sequence in the first voltage candidate vector sequence and the second voltage candidate vector sequence based on the first objective function value and the second objective function value.

[0130] Specifically, according to the preset objective function shown in the above relational expression (19), the first objective function value of the first voltage candidate vector sequence and the second objective function value of the second voltage candidate vector sequence are calculated respectively.

[0131] Further, the first objective function value and the second objective function value are compared, and if the first objective function value is greater than the second objective function value, the first voltage candidate vector sequence is used as the target voltage candidate vector sequence. Conversely, if the first objective function value is less than the second objective function value, the second voltage candidate vector sequence is used as the target voltage candidate vector sequence.

[0132] Step S3043: determining a target duty cycle of the target voltage candidate vector sequence based on the first duty cycle and the second duty cycle.

[0133] Specifically, if the target voltage candidate vector sequence is the first voltage candidate vector sequence, the first duty cycle is used as the target duty cycle; if the target voltage candidate vector sequence is the second voltage candidate vector sequence, the second duty cycle is used as the target duty cycle.

[0134] Step S3044: generating a drive signal for each switch tube for controlling the current of the energy storage converter based on the target voltage candidate vector sequence and the target duty cycle.

[0135] Specifically, the obtained target voltage candidate vector sequence and target duty cycle are used as the optimal output information of the energy storage converter control system, and the drive signal of each switch tube of the rectifier is generated according to the optimal output information. The optimal output information is the drive signal of the switch tube that minimizes the current harmonics.

[0136] The energy storage converter model-free predictive current control method provided in this embodiment can construct a corresponding improved target super-local model by combining multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals on the AC side of the energy storage converter in multiple historical control cycles. Secondly, by combining the target super-local model and the improved generalized symmetric dual-vector modulation method, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the corresponding reference voltage sector can be obtained. Compared with the traditional symmetric dual-vector scheme, by introducing the generalized dual vector, a better steady-state effect can be obtained under a high modulation ratio, and the harmonic content is greatly reduced. Finally, on the basis of considering the preset objective function of frequency control, the target voltage candidate vector sequence is determined in the two obtained voltage candidate vector sequences, and the target duty cycle of the target voltage candidate vector sequence is combined to generate a drive signal for each switch tube for controlling the current of the energy storage converter. By introducing the candidate vector sequence and adding frequency control to the preset objective function, the switching frequency can be effectively controlled. Therefore, by implementing the present invention, by combining the improved generalized symmetric dual-vector modulation method with model-free predictive control, the steady-state performance of the model-free predictive control of the energy storage converter is effectively improved.

[0137] In one example, a method for model-free predictive current control of an energy storage converter based on an improved generalized symmetric dual vector is provided, comprising:

[0138] S1: The grid voltage, grid current and AC output voltage of the converter are sampled by 2 / 3 transformation to obtain the grid voltage, grid current and AC output voltage signals of the converter in the two-phase stationary coordinate system, and the grid current difference Δi is calculated based on the grid current signals of the past two control cycles. k and Δi k-1 , substitute into the improved hyperlocal model to solve the parameters F and α in the hyperlocal model.

[0139] S2: Based on the improved hyperlocal model with known parameters obtained in step S1, combined with the grid voltage signal, active power and reactive power given values, the grid current reference value i is calculated. ref and the converter side reference voltage vector v ref.

[0140] S3: Obtain the reference voltage vector v according to step S2 ref , and then find the sector where the reference voltage is located and the basic voltage vector corresponding to the sector. According to SVM modulation, two vectors u of the synthetic three-segment vector sequence are obtained x 、u y And the corresponding duty cycle t x ,t y In one control cycle, u x As the first and last vectors of the three-segment vector sequence, the corresponding u y Then placing it in the middle can form the first candidate vector sequence u x ,u y ,u x , similarly, for u y Similar processing can be done to obtain the second candidate vector sequence u y ,u x ,u y .

[0141] S4: Take the first candidate vector sequence u x ,u y ,u x For example, when calculating the duty cycle of each vector in the three-segment sequence, the proportional coefficient K is introduced to form the actual duty cycle of each segment of the three-segment vector sequence (K×tx, ty, 1-Ktx, according to the current harmonic calculation formula, the current harmonic value of the three-segment candidate vector sequence can be calculated, and the expression of the square of the effective value of the current harmonic is a quadratic equation about K, and the opening is upward. When the expression takes a minimum value, the corresponding horizontal coordinate is the optimal K value. In this way, the optimal duty cycle of each vector in the candidate vector sequence can be obtained.

[0142] S5: Calculate the objective function corresponding to each candidate vector sequence Select u to minimize the objective function value x ,u y ,u x and the corresponding duty cycle (K×t x , t y , (1-K)t x ) as the optimal output of the control system. The driving signal of each switch tube of the rectifier is generated according to the optimal output information of the control system.

[0143] Further, in an optional embodiment, if Figure 4 and Figure 5 As shown, from top to bottom the waveforms are active power, reactive power, three-phase voltage on the AC side, and three-phase current on the AC side. Figure 4 and Figure 5It can be concluded from the comparison that the method of model-free predictive current control of energy storage converter based on improved generalized symmetric dual-vector provided in this example greatly reduces the ripple of active power and reactive power compared with the traditional method, and makes the AC side current closer to sinusoidal.

[0144] Further, if Figure 6 and Figure 7 The following are the current THD analysis diagrams of the traditional symmetrical dual vector model predictive current control and the current control based on the improved generalized symmetrical dual vector model predictive current control under high modulation ratio conditions when the sampling rate is 10kHz. Figure 6 and Figure 7 It can be found that the current THD of the traditional method is 8.5608%, while the current THD of the method of model-free predictive current control of energy storage converter based on improved generalized symmetric dual-vector provided in this example is 4.61%. Compared with the traditional method, the method proposed in this example can reduce the current THD by 46.1% under high modulation ratio.

[0145] Further, if Figure 8 As shown in the figure, it is a comparison bar chart of the current THD of the traditional symmetrical dual-vector model predictive current control and the model-free predictive current control based on the improved generalized symmetrical dual-vector at the same switching frequency under four working points (the reference active power is 600w, 700w, 800w, and 900w respectively). Figure 8 It can be seen that the higher the modulation ratio, the more obvious the effect of reducing current THD by the method provided in this example compared with the traditional symmetrical dual-vector model predictive current control.

[0146] Therefore, the method for model-free predictive current control of energy storage converter based on improved generalized symmetric dual vector provided in this example has the following beneficial effects:

[0147] 1. Compared with the traditional symmetrical dual-vector solution, the introduction of generalized dual-vector can achieve better steady-state effect under high modulation ratio and significantly reduce harmonic content.

[0148] 2. The deadbeat principle is used to select candidate vector sequences and calculate the duty cycle, and the optimal vector duration is obtained by optimizing current harmonics, so that the selected sequence achieves the best steady-state performance.

[0149] 3. The introduction of candidate sequences and the addition of frequency control to the objective function can effectively control the switching frequency.

[0150] 4. The improved generalized symmetric dual-vector modulation method is combined with model-free predictive control to effectively improve the steady-state performance of model-free predictive control.

[0151] In this embodiment, a model-free predictive current control device for an energy storage converter is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0152] This embodiment provides a model-free predictive current control device for an energy storage converter, such as Fig. 9 As shown, the device comprises:

[0153] An acquisition module 901 is used to acquire multiple target grid voltage signals, multiple target grid current signals, and multiple target output voltage signals of the AC side of the energy storage converter within multiple historical control cycles;

[0154] A construction module 902 is used to construct an improved target hyperlocal model based on multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals;

[0155] The processing module 903 is used to obtain a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located based on multiple target grid current signals, preset active power and preset reactive power, through processing by a target super-local model and an improved generalized symmetric dual-vector modulation method;

[0156] The generation module 904 is used to generate a drive signal for each switch tube for controlling the current of the energy storage converter based on a preset objective function using the first voltage candidate vector sequence and the second voltage candidate vector sequence. The preset objective function is used to characterize the optimization target taking frequency control into account.

[0157] In some optional implementations, the construction module 902 includes:

[0158] The acquisition submodule is used to obtain an improved initial hyperlocal model.

[0159] The first calculation submodule is used to calculate a plurality of grid current differences within a plurality of historical control cycles based on a plurality of target grid current signals.

[0160] The second calculation submodule is used to calculate a model parameter set of the initial hyperlocal model based on multiple target grid voltage signals, multiple target output voltage signals and multiple grid current differences after being processed by the initial hyperlocal model.

[0161] The first determination submodule is used to determine a target hyperlocal model based on an initial hyperlocal model and a model parameter set.

[0162] In some optional implementations, the processing module 903 includes:

[0163] The third calculation submodule is used to calculate a grid current reference value based on a preset active power and a preset reactive power.

[0164] The first processing submodule is used to calculate a plurality of converter side reference voltage values ​​based on a plurality of target grid current signals and grid current reference values ​​after being processed by a target super-local model.

[0165] The second processing submodule is used to obtain a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located based on multiple converter side reference voltage values ​​through an improved generalized symmetric dual-vector modulation method.

[0166] In some optional implementations, the second processing submodule includes:

[0167] The first calculation unit is used to calculate the first non-zero basic vector action time value, the second non-zero basic vector action time value and the zero vector action time value of the sector where the reference voltage is located by using the deadbeat principle.

[0168] The first determining unit is configured to determine a first voltage candidate vector sequence and a second voltage candidate vector sequence based on a first non-zero basic vector action time value, a second non-zero basic vector action time value, and a zero vector action time value.

[0169] In some optional implementations, the generating module 904 includes:

[0170] The fourth calculation submodule is configured to calculate a first duty cycle of the first voltage candidate vector sequence and a second duty cycle of the second voltage candidate vector sequence.

[0171] The second determining submodule is configured to determine a target voltage candidate vector sequence from the first voltage candidate vector sequence and the second voltage candidate vector sequence based on a preset objective function.

[0172] The third determining submodule is configured to determine a target duty cycle of the target voltage candidate vector sequence based on the first duty cycle and the second duty cycle.

[0173] The generating submodule is used to generate a driving signal for each switch tube for controlling the current of the energy storage converter based on the target voltage candidate vector sequence and the target duty cycle.

[0174] In some optional implementations, the second determining submodule includes:

[0175] The second calculation unit is used to calculate a first objective function value of the first voltage candidate vector sequence and a second objective function value of the second voltage candidate vector sequence based on a preset objective function.

[0176] The second determining unit is configured to determine a target voltage candidate vector sequence in the first voltage candidate vector sequence and the second voltage candidate vector sequence based on the first objective function value and the second objective function value.

[0177] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0178] The model-free predictive current control device of the energy storage converter in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0179] The embodiment of the present invention also provides a computer device having the above Fig. 9 The energy storage converter shown has no model predictive current control device.

[0180] See also Fig.10 , Fig.10 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.10 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 10 is taken as an example.

[0181] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0182] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0183] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0184] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0185] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0186] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0187] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0188] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A model-free predictive current control method for an energy storage converter, characterized in that: The method comprises: Acquire multiple target grid voltage signals, multiple target grid current signals, and multiple target output voltage signals of the AC side of the energy storage converter within multiple historical control cycles; constructing an improved target hyperlocal model based on the multiple target grid voltage signals, the multiple target grid current signals, and the multiple target output voltage signals; Based on the multiple target grid current signals, the preset active power and the preset reactive power, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located are obtained through processing by the target super-local model and the improved generalized symmetric dual-vector modulation method; Based on a preset objective function, the first voltage candidate vector sequence and the second voltage candidate vector sequence are used to generate a drive signal for each switch tube for controlling the current of the energy storage converter, and the preset objective function is used to characterize an optimization target that takes frequency control into consideration.

2. The method according to claim 1, characterized in that Based on the multiple target grid voltage signals, the multiple target grid current signals and the multiple target output voltage signals, an improved target hyperlocal model is constructed, comprising: obtaining an improved initial hyperlocal model; Calculating a plurality of grid current difference values ​​within the plurality of historical control cycles based on the plurality of target grid current signals; Based on the multiple target grid voltage signals, the multiple target output voltage signals and the multiple grid current difference values, after being processed by the initial hyperlocal model, a model parameter set of the initial hyperlocal model is calculated; The target hyperlocal model is determined based on the initial hyperlocal model and the model parameter set.

3. The method according to claim 1, characterized in that Based on the multiple target grid current signals, the preset active power and the preset reactive power, a first voltage candidate vector sequence and a second voltage candidate vector sequence of the sector where the reference voltage is located are obtained through processing by the target super-local model and the improved generalized symmetric dual-vector modulation method, including: Calculating a grid current reference value based on the preset active power and the preset reactive power; Based on the multiple target grid current signals and the grid current reference value, after being processed by the target super-local model, a plurality of converter side reference voltage values ​​are calculated; Based on the multiple converter-side reference voltage values, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the sector where the reference voltage is located are obtained through processing by an improved generalized symmetric dual-vector modulation method.

4. The method according to claim 3, characterized in that Based on the multiple converter side reference voltage values, the first voltage candidate vector sequence and the second voltage candidate vector sequence of the sector where the reference voltage is located are obtained by processing with an improved generalized symmetric dual vector modulation method, including: Calculate the first non-zero basic vector action time value, the second non-zero basic vector action time value and the zero vector action time value of the sector where the reference voltage is located by using the deadbeat principle; The first voltage candidate vector sequence and the second voltage candidate vector sequence are determined based on the first non-zero basic vector action time value, the second non-zero basic vector action time value, and the zero vector action time value.

5. The method according to claim 1, characterized in that: Based on a preset objective function, using the first voltage candidate vector sequence and the second voltage candidate vector sequence, generating a drive signal for each switch tube of the energy storage converter, including: calculating a first duty cycle of the first voltage candidate vector sequence and a second duty cycle of the second voltage candidate vector sequence; Based on the preset objective function, determining a target voltage candidate vector sequence in the first voltage candidate vector sequence and the second voltage candidate vector sequence; Determining the target duty cycle of the target voltage candidate vector sequence based on the first duty cycle and the second duty cycle; Based on the target voltage candidate vector sequence and the target duty cycle, a drive signal for each switch tube for controlling the current of the energy storage converter is generated.

6. The method according to claim 5, characterized in that Determining a target voltage candidate vector sequence in the first voltage candidate vector sequence and the second voltage candidate vector sequence based on the preset objective function includes: Based on the preset objective function, calculating a first objective function value of the first voltage candidate vector sequence and a second objective function value of the second voltage candidate vector sequence; The target voltage candidate vector sequence is determined in the first voltage candidate vector sequence and the second voltage candidate vector sequence based on the first objective function value and the second objective function value.

7. A model-free predictive current control device for an energy storage converter, characterized in that: The device comprises: An acquisition module, used to acquire multiple target grid voltage signals, multiple target grid current signals and multiple target output voltage signals of the AC side of the energy storage converter within multiple historical control cycles; A construction module, configured to construct an improved target hyperlocal model based on the plurality of target grid voltage signals, the plurality of target grid current signals and the plurality of target output voltage signals; a processing module, configured to obtain a first voltage candidate vector sequence and a second voltage candidate vector sequence of a sector where a reference voltage is located based on the multiple target grid current signals, the preset active power and the preset reactive power, through processing by the target super-local model and the improved generalized symmetric dual-vector modulation method; A generation module is used to generate a drive signal for each switch tube for controlling the current of the energy storage converter based on a preset objective function using the first voltage candidate vector sequence and the second voltage candidate vector sequence, wherein the preset objective function is used to characterize an optimization target that takes frequency control into consideration.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the model-free predictive current control method for an energy storage converter according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the model-free predictive current control method for an energy storage converter according to any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises computer instructions, and the computer instructions are used to enable a computer to execute the model-free predictive current control method for an energy storage converter according to any one of claims 1 to 6.

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