Energy storage converter controller optimization method, device, storage medium and product

Abstract

The present invention discloses an optimization method, device, storage medium and product for a energy storage converter controller. The method includes designing an energy storage converter controller, which includes an outer loop and an inner loop, and the inner loop is a deadbeat controller; establishing a mathematical model of the deadbeat controller; determining a recurrence expression of the identification parameters according to the constructed optimization objective function; when it is judged to start parameter identification calculation according to the command current and the actual output current at the current moment, calculating the identification parameters at the current moment according to the current moment current data and voltage data of the main circuit of the energy storage converter and the recurrence expression of the identification parameters; when it is judged to converge according to the identification parameters at the current moment and the previous moment, substituting the identification parameters at the current moment into the mathematical model of the deadbeat controller to realize the online optimization process of the deadbeat controller. The present invention performs online optimization on the mathematical model of the deadbeat controller, improving the system control accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage control, and particularly relates to an optimization method, device, storage medium and product for a controller of an energy storage converter based on inductance parameter identification. Background Technique

[0002] The basic function of an energy storage system is to achieve the bidirectional flow of energy between an energy storage battery and the power grid. The key device for realizing the bidirectional energy conversion in the energy storage system is the energy storage converter. Therefore, the energy storage converter is the core of energy conversion in the entire energy storage system, and its importance is self-evident. The energy storage converter is a power electronic device, and its main function is to convert electrical energy between the energy storage battery and the power grid, and can realize the charging and discharging processes of electrical energy to ensure the efficient operation of the energy storage system. When the energy storage system is charging, the energy storage converter converts the three-phase alternating current of the power grid into direct current to charge the energy storage battery; when the energy storage system is discharging, the energy storage converter converts the direct current of the battery into three-phase alternating current and sends it into the power grid, so as to realize the bidirectional flow of energy between the power grid and the energy storage battery.

[0003] At present, the main circuit topologies of energy storage converters applied in the industry mainly include two-level topology, three-level topology, cascaded H-bridge topology, modular multilevel topology and full-bridge converters, and the hardware circuit structures are basically mature. The key lies in the research of the control algorithm of the energy storage converter. The wide application of high-speed digital signal processing chips DSP in the field of power electronics provides a hardware foundation for the implementation of more intelligent algorithms. Therefore, the controllers of energy storage converters mostly adopt DSP control chips with high-speed digital signal processing capabilities, which can achieve high-precision tracking of target signals, and have the advantages of fast dynamic response, simple parameter tuning and easy implementation of algorithms.

[0004] The controller design of the energy storage converter mostly adopts a double - closed - loop controller. According to different operating conditions of the device, the outer loop can be a DC bus voltage outer loop or a power outer loop, and the inner loop is always a current inner loop. The function achieved by the DC bus voltage outer loop is to maintain the voltage across the bus capacitor of the energy storage converter at a set value. The function achieved by the power outer loop is to make the actual output / input power of the system track the change of the reference power. Whether it is the voltage outer loop or the power outer loop, the control quantity is a DC quantity. According to classical control theory, using PID control can achieve error - free tracking control of the DC quantity. In the double - closed - loop controller, the output of the outer loop is the reference current value of the energy storage converter. The current inner loop receives the reference current value given by the outer loop and also receives the real - time output current value of the AC side of the energy storage converter collected by the current transformer. The reference current value and the real - time output current value are simultaneously sent to the current inner - loop controller for control algorithm operation, and the PWM control instruction of the energy storage converter is output. The power module IGBT of the main circuit receives the PWM control instruction, and the energy storage converter outputs three - phase grid - connected current. In the next moment, the DC bus voltage value or power value of the main circuit and the three - phase grid - connected current value collected are sent into the control system again, thus forming a complete feedback control system.

[0005] There are many design methods for the current inner - loop controller, such as PI control, PR control, dead - beat control, and more advanced intelligent control algorithms, etc. The dead - beat control algorithm has the advantage of being easy to implement digitally. With the development of digital technology, the dead - beat control algorithm can be easily implemented through a digital controller (DSP), reducing the complexity and cost of system design, and is more and more widely used in engineering applications. The energy storage converter adopts a power or voltage outer - loop PI control and a current inner - loop dead - beat control strategy, with a stable control system and convenient parameter adjustment. The outer - loop PI control ensures the stability of the DC bus voltage or output power through proportional and integral regulation, eliminates the steady - state error, and improves the system stability; while the current inner - loop dead - beat control uses an accurate mathematical model to predict the future current, achieving fast response and accurate control, effectively solving the system delay problem and improving the dynamic performance. The combination of the two enables the energy storage converter to reach a relatively high level in voltage, power, and current control, meeting the requirements of basic application scenarios.

[0006] Research on the controller of the energy storage converter using voltage / power outer - loop PI control and current inner - loop dead - beat control finds that the controller designed based on this method has great advantages in DSP application, but at the same time, there are also obvious disadvantages, mainly the following two points:

[0007] (1) The core of deadbeat control lies in an accurate mathematical model. The lack of an accurate model will make it difficult for the control algorithm to accurately predict the system behavior, which will further lead to poor control effects and a decline in system stability. Therefore, when applying deadbeat control, it is necessary to ensure that the mathematical model relied on is as accurate as possible. In the process of establishing the mathematical model of deadbeat control for the energy storage converter, the measured value of the inductor of the main circuit of the energy storage converter is required. However, the traditional method directly substitutes the inductor parameters provided by the inductor supplier as known quantities into the mathematical model. The inductor parameters provided by the supplier are the inductance values when the inductor is unloaded, which deviate greatly from the inductance values when the inductor is loaded. At the same time, when the inductor operates under actual working conditions, its inductance value changes in real time. At this time, the deadbeat mathematical model established based on the inductor parameters provided by the supplier has a large deviation, directly resulting in the output current of the energy storage converter being unable to track the change of the target current, with a large tracking error, thus leading to the distortion of the output current waveform and an increase in harmonic content, affecting the output power quality of the energy storage converter and the overall performance of the system. Therefore, ensuring the accuracy of the inductor parameters is the key to achieving efficient deadbeat control. The sources of inductor parameter errors mainly include two aspects:

[0008] (a) The inductor parameters provided by the supplier deviate from the actual values and are not real enough;

[0009] (b) The inductor parameters provided by the supplier are no-load parameters. When the energy storage converter operates, the inductor will face situations such as overload and magnetic saturation, and the inductor parameters will change continuously. Specifically, in the actual operation process of the energy storage converter, the inductor value is not constant, but is affected by various external and internal factors such as temperature rise, equipment aging, and current fluctuation and changes dynamically. However, in deadbeat control, if the inductor parameters fail to reflect these real-time changes in a timely manner and remain a static and fixed value, then there will inevitably be a deviation between the mathematical model of deadbeat control and the actual operation situation. This deviation in the mathematical model will directly lead to a deviation in the deadbeat control command, resulting in the system output current being unable to track the change of the target current, further exacerbating the waveform distortion of the current and increasing the harmonic content of the current, which has an adverse impact on the overall performance and stability of the system.

[0010] (2) Applying the parameter identification technology to the optimization of the deadbeat controller parameters requires considering the actual code implementation. If the optimal parameters cannot converge quickly, it will lead to a large computing pressure on the processor and a large amount of data storage. Therefore, it is necessary to design the convergence conditions for the parameter identification operation. In addition, during the operation of the device, according to the different actual working conditions, there should be judgment conditions for the input and cut-off of the parameter identification operation. Otherwise, when the device starts with a pre-charge resistor or a transient fault or even a permanent fault occurs, at this time, because the system is in a non-steady working state and the mathematical model of the system has changed, the inductance parameter identification is inaccurate, and the wrong optimal inductance parameter will even cause the entire control system to collapse. Therefore, under non-steady working conditions, the execution of the inductance parameter identification needs to be stopped. Summary of the Invention

[0011] The purpose of the present invention is to provide an energy storage converter controller optimization method, device, storage medium and product, so as to solve the problem that when the current inner loop is deadbeat controlled, due to the change of the inductance parameter, it is difficult to accurately establish the mathematical model of the deadbeat control, which in turn leads to poor control effect and the decline of the performance and stability of the energy storage system.

[0012] The present invention solves the above technical problems through the following technical solutions: An energy storage converter controller optimization method includes:

[0013] Establish a mathematical model of the energy storage converter according to the main circuit topology of the energy storage converter and Kirchhoff's voltage law;

[0014] Design an energy storage converter controller, the energy storage converter controller is a double closed-loop controller, the double closed-loop controller includes an outer loop and an inner loop, and the inner loop is a deadbeat controller;

[0015] Establish a mathematical model of the deadbeat controller according to the mathematical model of the energy storage converter and the discretization principle;

[0016] Construct an optimization objective function according to the mathematical model of the energy storage converter, and determine the recurrence expression of the identification parameters according to the optimization objective function;

[0017] Obtain the command current at the current moment and the actual output current of the energy storage converter at the current moment, and judge whether to start the parameter identification calculation according to the command current and the actual output current at the current moment;

[0018] When starting the parameter identification calculation, obtain the current data and voltage data of the main circuit of the energy storage converter at the current moment;

[0019] Calculate the identification parameters at the current moment according to the current data and voltage data of the main circuit of the energy storage converter at the current moment and the recurrence expression of the identification parameters;

[0020] Judge whether it converges according to the identification parameters at the current moment and the previous moment; if so, substitute the identification parameters at the current moment into the mathematical model of the deadbeat controller to realize the online optimization process of the deadbeat controller; if not, repeat the parameter identification calculation process.

[0021] Further, when the main circuit topology of the energy storage converter is a two-level three-phase three-leg split capacitor topology, the mathematical model of the energy storage converter is:

[0022]

[0023]

[0024] Among them, u jo represents the voltage of the midpoint of the j-th leg relative to the midpoint O of the DC-side bus capacitor, j = a, b, c, and the legs a, b, c correspond to the A, B, C phases of the distribution network one by one; e A , e B , e C respectively represent the three-phase grid voltages of the distribution network; R LA , R LB , R LC respectively represent the internal resistances of the three-phase filter inductors on the AC side; i aA , i bB , i cC respectively represent the currents input by the midpoints of each leg to the corresponding phase grid; L A , L B , L c respectively represent the inductances of the three-phase filter inductors on the AC side; C1 and C2 respectively represent the capacitances of the upper and lower DC-side bus capacitors; U dc1 , U dc2 respectively represent the terminal voltages of the upper and lower DC-side bus capacitors; S j represents the switching state of the j-th leg; U dc represents the DC-side bus voltage.

[0025] Further, the outer loop is a power outer loop or a voltage outer loop, and the outer loop adopts a PI controller.

[0026] Further, when the main circuit topology of the energy storage converter is a two-level three-phase three-leg split capacitor topology, the mathematical model of the deadbeat controller is:

[0027]

[0028] Among them, S j (k) represents the switching state of the j-th leg at the k-th moment, j = a, b, c, and the legs a, b, c correspond to the A, B, C phases of the distribution network one by one; U dcdenotes the DC-side bus voltage; L A 、L B 、L c respectively denote the inductance values of the three-phase filter inductors on the AC side; T S denotes the sampling period; respectively denote the three-phase reference currents at the k-th moment; i aA (k), i bB (k), i cC (k) respectively denote the currents of each bridge arm midpoint inputting to the corresponding phase power grid at the k-th moment; R LA 、R LB 、R LC respectively denote the internal resistances of the three-phase filter inductors on the AC side; e A (k), e B (k), e C (k) respectively denote the three-phase power grid voltages of the distribution network at the k-th moment.

[0029] Furthermore, the recursive expression for determining the identification parameters is obtained by using the recursive least squares method with a forgetting factor; where, the recursive expression for the identification parameters is:

[0030]

[0031]

[0032] where, denotes the recursive estimated value of θ km , θ km denotes the identification parameter matrix of the m-th phase at the k-th moment, denotes the recursive estimated value of θ k-1,m , θ k-1,m denotes the identification parameter matrix of the m-th phase at the (k - 1)-th moment, m = A, B, C; β km 、H k-1,m 、 all denote intermediate quantities; p km denotes the input quantity of the m-th phase at the k-th moment; q km denotes the output quantity of the m-th phase at the k-th moment; λ denotes the forgetting factor; p 1m denotes the input quantity of the m-th phase at the 1st moment; the superscript T denotes transpose.

[0033] Furthermore, the determination condition for starting the parameter identification calculation is:

[0034] When 0 ≤ I mse < δ, start the parameter identification calculation; when I mse ≥ δ, prohibit the parameter identification calculation;

[0035] where, δ denotes the error threshold, I mseRepresents the real-time current tracking error of the deadbeat controller; the real-time current tracking error I mse is:

[0036]

[0037] wherein, represents the m-phase command current of the energy storage converter at the k-th moment, n represents the number of sampling points within a power frequency period, n = f / 50, f represents the sampling frequency; i jm (k) represents the current of the m-phase power grid input at the midpoint of the j-th bridge arm at the k-th moment, and the bridge arms a, b, c correspond to the A, B, C phases of the distribution network one by one.

[0038] Furthermore, determining whether to converge according to the identification parameters at the current moment and the previous moment, and its convergence condition is:

[0039]

[0040] wherein, represents the recursive estimated value of θ km (i), θ km (i) represents the i-th parameter in the identification parameter matrix of the m-phase at the k-th moment, represents the recursive estimated value of θ k-1,m (i), θ k-1,m (i) represents the i-th parameter in the identification parameter matrix of the m-phase at the (k - 1)-th moment; T S represents the sampling period; ζ represents the convergence threshold.

[0041] Based on the same concept, the present invention provides an electronic device, including a memory, a processor, and a computer program / instructions stored on the memory, and the processor executes the computer program / instructions to implement the energy storage converter controller optimization method as described above.

[0042] Based on the same concept, the present invention provides a computer-readable storage medium, on which a computer program / instructions is stored, and when the computer program / instructions is executed by a processor, the energy storage converter controller optimization method as described above is implemented.

[0043] Based on the same concept, the present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions is executed by a processor, the energy storage converter controller optimization method as described above is implemented.

[0044] Beneficial effects

[0045] Compared with the prior art, the advantages of the present invention are:

[0046] The present invention constructs a mathematical model of a deadbeat controller based on the mathematical model of a energy storage converter and the discretization principle, and then uses a parameter identification algorithm to optimize the mathematical model of the deadbeat controller online, improving the system control accuracy and solving the problem that it is difficult to accurately establish the mathematical model of deadbeat control due to the change of inductance parameters. The present invention determines the opening judgment condition of the parameter identification algorithm according to the command current and the actual output current at the current moment, and also determines the convergence condition according to the identification parameters at the current moment and the previous moment, that is, by judging the change amount of the identification parameters within a unit control period, achieving the balance between the optimal parameter calculation and the operation speed.

[0047] The present invention uses the recursive least squares method with a forgetting factor for parameter identification calculation. By introducing a recursive mechanism, the data storage and calculation amount are significantly reduced, and thus the effective improvement of the parameter identification efficiency is realized. On this basis, a forgetting factor is introduced to dynamically adjust the weight of historical data, giving higher importance to new data, thereby endowing the parameter identification algorithm with the ability of self-correction and optimization, ensuring the double improvement of the accuracy and reliability of the identification parameters, optimizing the deadbeat control of the current inner loop, and solving the problem that the accuracy of the mathematical model of the inner loop deadbeat control is reduced due to the change of inductance parameters. The recursive least squares method with a forgetting factor is easy to be programmed and implemented, which not only simplifies the data processing flow, but also significantly enhances the adaptability of the system to dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only one embodiment of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 is the flow chart of the optimization method for the energy storage converter controller in the embodiment of the present invention;

[0050] Figure 2 is the structural schematic diagram of the two-level three-phase three-leg split capacitor topology in the embodiment of the present invention;

[0051] Figure 3 is the principle block diagram of the double closed-loop controller in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] The technical solutions of the present application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0054] As Figure 1 shown, an optimization method for a energy storage converter controller provided by an embodiment of the present invention includes the following steps:

[0055] Step 1: Establish a mathematical model of the energy storage converter according to the main circuit topology of the energy storage converter and Kirchhoff's voltage law.

[0056] Taking the main circuit topology of the energy storage converter as a two-level three-phase three-arm split capacitor topology as an example, its structural schematic diagram is as Figure 2 shown. Figure 2 In the figure, u jo represents the voltage of the midpoint of the j-th arm relative to the midpoint O of the DC-side bus capacitor, j = a, b, c, that is, u ao , u bo , u co respectively represent the voltages of the midpoints of arms a, b, c relative to the midpoint O of the DC-side bus capacitor; e m represents the m-th phase grid voltage of the distribution network, m = A, B, C, that is, e A , e B , e C respectively represent the grid voltages of phases A, B, C of the distribution network; R Lm represents the internal resistance of the m-th phase filter inductor on the AC side, m = A, B, C, that is, R LA , R LB , R LC respectively represent the internal resistances of the filter inductors of phases A, B, C on the AC side; L m represents the inductance of the m-th phase filter inductor on the AC side, m = A, B, C, that is, L A , L B , L c respectively represent the inductances of the filter inductors of phases A, B, C on the AC side; i jm represents the current input from the midpoint of the j-th arm to the m-th phase grid. Arms a, b, c correspond to phases A, B, C of the distribution network one by one, that is, when j = a, m = A; when j = b, m = B; when j = c, m = C; i aAThe current input from the midpoint of arm a to the A-phase power grid is \(i\). bB The current input from the midpoint of arm b to the B-phase power grid is \(i\). cC The current input from the midpoint of arm c to the C-phase power grid is \(i\). ON The current input from the midpoint O of the DC-side bus capacitor to the neutral line N is \(i\). \(C_1\) and \(C_2\) respectively represent the capacitances of the upper and lower DC-side bus capacitors; \(U\). dc1 and \(U\). dc2 respectively represent the terminal voltages of the upper and lower DC-side bus capacitors; \(U\). dc represents the DC-side bus voltage; \(VT1\) to \(VT6\) respectively represent the power modules of each arm.

[0057] Assume that the direction of the current flowing into the power grid is the positive direction of the current. According to the main circuit topology of the energy storage converter and the reference directions of voltage and current, use Kirchhoff's voltage law to establish a mathematical model of the three-phase four-wire energy storage converter. First, define the switching states of each arm:

[0058]

[0059] where \(S\). j represents the switching state of the \(j\)-th arm. Taking arm a as an example, when the power module \(VT1\) of the upper arm is conducting and the power module \(VT4\) of the lower arm is off, the switching state of arm a is 1; when the power module \(VT1\) of the upper arm is off and the power module \(VT4\) of the lower arm is conducting, the switching state of arm a is -1.

[0060] The voltage of each arm midpoint on the AC side of the energy storage converter relative to the midpoint O of the DC-side bus capacitor is:

[0061]

[0062] The switching states of the three arms are defined as \(S\). a and \(S\). b and \(S\). c , and each switching state can take two different values of 1 and -1. For a three-phase four-wire system, the neutral line impedance is ignored. Kirchhoff's voltage law is written for the main circuit of the energy storage converter, and its mathematical model can be obtained as:

[0063]

[0064] From the mathematical model of the energy storage converter, it can be seen that for a three-phase four-wire system, there is no coupling relationship between phases, and independent control of each phase current can be achieved.

[0065] Step 2: Design the energy storage converter controller. The energy storage converter controller is a double-loop controller. The double-loop controller includes an outer loop and an inner loop, and the inner loop is a deadbeat controller.

[0066] According to different equipment operating conditions, the outer loop can adopt a voltage outer loop or a power outer loop, and the outer loop controller uses a PI controller; the inner loop is designed as a current inner loop, and the inner loop uses a deadbeat controller.

[0067] The schematic diagram of the double closed-loop controller is as Figure 3 shown, where P s represents the output power of the energy storage converter, and P ref / U dcref represents the set active power command value / voltage command value. represents the three-phase command current of the energy storage converter (i.e., the reference current value). The difference between the active power command value and the output power of the energy storage converter (or the difference between the voltage command value and the DC side bus voltage) passes through the PI controller to obtain the active current command value i dref . The active current command value i dref passes through the dq-abc coordinate transformation to obtain the reference current value of the mth phase . The output of the outer loop is the reference current value of the energy storage converter. The current inner loop receives the reference current value given by the outer loop and also receives the real-time output current value i jm of the AC side of the energy storage converter collected by the current transformer. The reference current value and the real-time output current value are simultaneously fed into the deadbeat controller for control operation, and the PWM control command of the energy storage converter is output.

[0068] Step 3: According to the mathematical model of the energy storage converter and the discretization principle, establish the mathematical model of the deadbeat controller.

[0069] Combining formula (2) and formula (3), and performing forward difference discretization with the sampling period T S , the mathematical model of the deadbeat controller of the energy storage converter is obtained as:

[0070]

[0071] Among them, S j (k) represents the switching state of the jth bridge arm at the kth moment; i aA (k), i bB (k), i cC (k) respectively represent the currents of the corresponding phases of the power grid input to the midpoints of each bridge arm at the kth moment; i aA (k + 1), i bB (k + 1), i cC (k + 1) respectively represent the currents of the corresponding phases of the power grid input to the midpoints of each bridge arm at the (k + 1)th moment; e A (k), e B (k), e C (k) respectively represent the grid voltages of phases A, B, and C of the distribution network at the kth moment. The sampling period T SSmall enough.

[0072] The control objective of deadbeat control is to make the output current at the next moment track the command current value calculated at the current moment without error. It can be seen from Figure 3 that by sampling at the k-th moment, the command current value at the k-th moment is obtained through the outer-loop PI controller and dq transformation If the parameters of the deadbeat controller are reasonably designed, after one control cycle, that is, at the k + 1-th moment, the output current of the energy storage converter will track the command current value. Therefore, by the sampling flat-push method, let:

[0073]

[0074] Substituting formula (5) into formula (4), the final mathematical model of the deadbeat controller is obtained as:

[0075]

[0076] where, respectively represent the command currents of phases A, B, and C at the k-th moment.

[0077] Step 4: Construct an optimization objective function according to the mathematical model of the energy storage converter, and determine the recurrence expression of the identification parameters according to the optimization objective function.

[0078] According to the mathematical model of the deadbeat controller, the value of the main circuit inductor parameter has the greatest influence on the accuracy of the model. However, the common practice in engineering is to introduce a deadbeat control coefficient ψ to multiply with the inductor parameter L to achieve the purpose of correcting the inductor parameter. However, this method can only be adjusted by multiple experiments, and the obtained inductor parameter will result in poor control accuracy of the entire control system. Therefore, the present invention uses a recursive least squares method with a forgetting factor to identify the inductor parameter of the deadbeat controller.

[0079] Represent the input and output of the system in vector form:

[0080] Pθ = Q (7)

[0081] P = [p1, p2, …, p k T , Q = [q1, q2, …, q k T (8)

[0082] where, P represents the input matrix, p k represents the input quantity at the k-th moment, Q represents the output matrix, q k represents the output quantity at the k-th moment, and θ represents the identification parameter matrix, and θ can be obtained by parameter estimation through P and Q.

[0083] According to the mathematical model of the energy storage converter (i.e., formula (3)), to obtain the most accurate mathematical model, it is necessary to identify the inductance parameters of the three phases separately. For a three-phase four-wire system, there is no coupling relationship between the phases, and independent control of the phase currents can be achieved. Therefore, the identification of the inductance parameters of the three phases is calculated separately, that is, the identification process is to first identify the inductance parameters of phase A, then identify the inductance parameters of phase B, and finally identify the inductance parameters of phase C. Taking the identification of the inductance parameters of the m-th phase as an example, the output quantity q can be defined. m = u jo - e A , and the input quantity is the identified parameter θ m = [L m R Lm T . To facilitate code implementation, it is necessary to discretize the differential term of the input quantity. Using the sampling period T S for backward difference discretization, where T s = 1 / f (f is the sampling frequency of the control system):

[0084]

[0085] where, i jm (k) represents the current input to the m-th phase grid at the midpoint of the j-th bridge arm at the k-th moment, and i jm (k - 1) represents the current input to the m-th phase grid at the midpoint of the j-th bridge arm at the (k - 1)-th moment. Therefore, formula (7) becomes:

[0086]

[0087] where, u jo (k) represents the voltage at the midpoint of the j-th bridge arm relative to the midpoint O of the DC side bus capacitor; e m (k) represents the voltage of the m-th phase grid of the distribution network at the k-th moment.

[0088] The least squares method is an online learning method that aims to minimize the sum of the squared residuals between the observed value Q (obtained by sampling) and the model estimated value (where ), and continuously updates the model parameters to seek the optimal solution. Specifically, the optimization objective function for the m-th phase is:

[0089]

[0090] where, J represents the optimization target value; represents the estimated value of Q m , Q m represents the output matrix of the m-th phase, and P m ​denote the input matrix of the m-th phase, denote the estimated value of the identification parameter matrix θ of the m-th phase m .

[0091] Select the identification parameter matrix θ that can minimize the optimization objective function m , so that the estimated value of the deadbeat controller output can be closer to the system observation value (actual sampling value), that is, the optimal estimation is completed. Let the optimization objective function be equal to 0, and the expression of the identification parameter matrix θ is obtained by calculation and solution m :

[0092]

[0093] To implement the parameter identification algorithm on a computer, the data in matrix Q m and Q m will continuously expand as new data is sampled. Define that after sampling and obtaining new data at the k-th moment, matrix P m is P km , matrix Q m is Q km . Essentially, P km and Q km are obtained by vertically stacking new data and old data. Then there are:

[0094]

[0095] where P km denotes the input matrix of the m-th phase at the k-th moment, P k-1,m denotes the input matrix of the m-th phase at the (k - 1)-th moment, p km denotes the input quantity of the m-th phase at the k-th moment, Q km denotes the output matrix of the m-th phase at the k-th moment, Q k-1,m denotes the output matrix of the m-th phase at the (k - 1)-th moment, q km denotes the output quantity of the m-th phase at the k-th moment.

[0096] To avoid re-calculating the least squares method using formula (12) due to the addition of new sampling data during the parameter identification process using the least squares method, the recursive least squares method, which is more suitable for computer operation, is adopted. Each round uses recursive solution to avoid large-scale matrix multiplication operations, minimize the occupancy of chip computing power as much as possible, and maintain the real-time nature of the calculation.

[0097] Define the intermediate quantity H km as:

[0098]

[0099] According to formulas (12), (13), (14) and (15), the recursive estimated value of the identification parameter matrix of the m-th phase at the k-th moment can be obtained as follows: It is:

[0100]

[0101] β km = H km p km (17)

[0102] Wherein, represents the recursive estimated value of the identification parameter matrix of the m-th phase at the (k - 1)-th moment.

[0103] In the parameter identification process of the recursive least squares method, in order to effectively prevent new data from being overwhelmed by a large amount of historical data (especially old data), so as to ensure that the system model can more accurately reflect the dynamic characteristics of the current system, the concept of forgetting factor is introduced. The recursive least squares method with forgetting factor adjusts the weights of historical data, making newer data account for a larger proportion in parameter estimation, while the influence of old data gradually weakens. The formula for parameter identification of the recursive least squares method with forgetting factor is:

[0104]

[0105] q km = u jo (k) - e m (k) (23)

[0106] Wherein, represents the recursive estimated value of θ km , θ km represents the identification parameter matrix of the m-th phase at the k-th moment, represents the recursive estimated value of θ k-1,m , θ k-1,m represents the identification parameter matrix of the m-th phase at the (k - 1)-th moment, m = A, B, C; β km , H k-1,m , all represent intermediate quantities; λ represents the forgetting factor, and its value is between 0 and 0.9; p 1m represents the input quantity of the m-th phase at the 1st moment.

[0107] The parameter identification algorithm based on the recursive least squares method is adopted. This algorithm seeks the optimal solution of parameters through the idea of minimizing the square of the error. Compared with search algorithms such as particle swarm, the algorithm is simpler and has a faster convergence speed. In the process of continuous iteration, as more and more sampling data is input, new data accumulates. Therefore, a forgetting factor is introduced on the basis of the recursive least squares method to reduce the weight of old data, enhance the role of new data, improve the adaptive ability of the parameter identification algorithm, and thus continuously improve the accuracy of parameter identification.

[0108] Step 5: Obtain the command current at the current moment and the actual output current of the energy storage converter at the current moment, and determine whether to start parameter identification calculation based on the command current and the actual output current at the current moment.

[0109] For the identification of the inductor parameters of the deadbeat controller, it is necessary to clarify the parameter identification start condition. During the startup process of the energy storage converter or when the energy storage converter fails, the main circuit topology will change, resulting in a fundamental change in the mathematical model of the system. At this time, it is of little significance to optimize the deadbeat controller, and incorrect optimization of the deadbeat parameters may even accelerate the collapse of the entire control system and expand the fault range. Therefore, the present invention uses the real-time current tracking error of the deadbeat controller as the judgment condition, that is, whether the actual current value obtained by the energy storage converter through the control command output by the deadbeat controller tracks the given command current value. When the judgment condition is met, the program executes the deadbeat inductor parameter identification to optimize the mathematical model of the deadbeat controller. When the judgment condition is not met, it is determined that the energy storage converter controller is in an unsteady operation mode, that is, the transient mode or permanent fault situation in common startup states, overcurrent and other fault conditions. Define the error as:

[0110]

[0111] where, I mse represents the real-time current tracking error of the deadbeat controller, represents the command current of the m-th phase of the energy storage converter at the k-th moment, n represents the number of sampling points in a power frequency cycle, n = f / 50; i jm (k) represents the current input from the midpoint of the j-th bridge arm to the m-th phase power grid at the k-th moment, that is, the sampling value of the actual output current of the energy storage converter at the k-th moment.

[0112] The judgment condition for starting parameter identification calculation is:

[0113] When 0 ≤ I mse <δ, it is determined that the system is in a steady operation mode, and the parameter identification calculation program is started; where, δ represents the error threshold.

[0114] When I mseWhen it is ≥δ, it is determined that the system is in a transient process or a fault condition. At this time, parameter identification calculation is prohibited, and the inductance value remains unchanged or is updated to the initial set value of the system.

[0115] Step 6: When starting the parameter identification calculation, obtain the current data and voltage data of the main circuit of the energy storage converter at the current moment.

[0116] For the input matrix of the m-th phase, the recursive estimated value of the identification parameter matrix, the forgetting factor λ, and the intermediate quantity β km Perform initialization; collect the current data and voltage data of the main circuit of the energy storage converter at the k-th moment to obtain p km and q km and then obtain the input matrix P km and the output matrix Q km .

[0117] Step 7: Calculate the identification parameters at the current moment according to the current data and voltage data of the main circuit of the energy storage converter at the current moment and the recursive expression of the identification parameters.

[0118] Substitute the data obtained in Step 6 into Formulas (18) - (23) to calculate the identification parameter matrix at the current moment

[0119] Step 8: Judge whether it converges according to the identification parameters at the current moment and the previous moment; if so, substitute the identification parameters at the current moment into the mathematical model of the deadbeat controller to realize the online optimization processing of the deadbeat controller; if not, repeat the parameter identification calculation process until convergence.

[0120] To accelerate the convergence of the identification parameters and reduce the computing power occupation of the processor, the convergence condition of the identification parameters is given as:

[0121]

[0122] where ζ represents the convergence threshold; represents the recursive estimated value of θ km (i), θ km (i) represents the i-th parameter in the identification parameter matrix of the m-th phase at the k-th moment, represents the recursive estimated value of θ k-1,m (i), θ k-1,m (i) represents the i-th parameter in the identification parameter matrix of the m-th phase at the (k - 1)-th moment. For and Convergence judgment is performed on each parameter in, and when the parameter with the largest change rate satisfies Formula (25), it is judged that the identification parameters in the entire identification parameter matrix converge. After satisfying Formula (25), the corresponding Substitute it into the mathematical model of the deadbeat controller to achieve the online optimization of the deadbeat controller.

[0123] To reduce the number of algorithm iterations, accelerate the convergence speed of the optimal solution of the inductor parameters, and at the same time reduce the fluctuation of the inductor parameters and the data jitter of parameter identification caused by sampling data errors, the convergence condition of parameter identification (such as formula (25)) is added. After obtaining the real-time value of the inductor parameters, the parameters of the deadbeat controller are updated to increase the accuracy of its mathematical model.

[0124] Embodiment 2

[0125] The embodiment of the present invention also provides an electronic device, which includes: a memory, a processor, and a computer program / instructions stored on the memory. The processor executes the computer program / instructions to implement the energy storage converter controller optimization method in the embodiments of the present application.

[0126] Although not shown, the electronic device includes a processor, which can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) and / or the programs and / or data loaded from the storage part into the random access memory (RAM). The processor can be a multi-core processor or include multiple processors. In some embodiments, the processor may include a general main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processing unit (NPU), a digital signal processing unit (DSP), and so on. In the RAM, various programs and data required for device operation are also stored. The processor, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0127] The above-mentioned processor and memory are jointly used to execute the program / instructions stored in the memory. When the program / instructions are executed by a computer, they can implement the methods, steps, or functions described in the above embodiments.

[0128] Although not shown, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, they implement the energy storage converter controller optimization method in the embodiments of the present application.

[0129] The storage medium in an embodiment of the present invention includes permanent and non-permanent, removable and non-removable articles that can implement information storage by any method or technology. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0130] A readable storage medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media do not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0131] Although not shown, an embodiment of the present invention also provides a computer program product, including: a computer program / instructions, which when executed by a processor, implement the energy storage converter controller optimization method in the embodiments of the present application.

[0132] The above-disclosed is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or variations, which should all be covered within the protection scope of the present invention.

Claims

1. A method for optimizing an energy storage converter controller, characterized in that: The optimization method comprises: According to the main circuit topology of the energy storage converter and Kirchhoff's voltage law, a mathematical model of the energy storage converter is established; Designing an energy storage converter controller, wherein the energy storage converter controller is a double closed-loop controller, the double closed-loop controller comprises an outer loop and an inner loop, and the inner loop is a deadbeat controller; According to the mathematical model of the energy storage converter and the discretization principle, a mathematical model of the deadbeat controller is established; Constructing an optimization objective function according to the mathematical model of the energy storage converter, and determining a recursive expression of an identification parameter according to the optimization objective function; Obtain the current command current and the actual output current of the energy storage converter at the current moment, and determine whether to start parameter identification calculation according to the current command current and the actual output current; When the parameter identification calculation is turned on, the current data and voltage data of the main circuit of the energy storage converter at the current moment are obtained; Calculating the identification parameters at the current moment according to the current data and voltage data of the main circuit of the energy storage converter at the current moment and the recursive expression of the identification parameters; Determine whether convergence is achieved according to the identification parameters at the current moment and the previous moment; if so, substitute the identification parameters at the current moment into the mathematical model of the deadbeat controller to achieve online optimization of the deadbeat controller; if not, repeat the parameter identification calculation process; Among them, when the main circuit topology of the energy storage converter is a two-level three-phase three-bridge arm split capacitor topology, the mathematical model of the deadbeat controller is: Among them, S j (k) represents the switch state of the jth bridge arm at the kth moment, j = a, b, c, and the bridge arms a, b, c correspond to the A, B, C phases of the distribution network one by one; U dc Indicates the DC bus voltage; L A , L B , L c Respectively represent the inductance of the three-phase filter inductance on the AC side; T S Indicates the sampling period; They represent the three-phase command currents at the kth moment respectively; i aA (k), i bB (k), i cC (k) represents the current of the corresponding phase grid input into the midpoint of each bridge arm at the kth moment; R LA , R LB , R LC They represent the internal resistance of the three-phase filter inductance on the AC side; e A (k), e B (k), e C (k) represent the three-phase grid voltage of the distribution network at the kth moment respectively.

2. The energy storage converter controller optimization method according to claim 1, characterized in that: When the main circuit topology of the energy storage converter is a two-level three-phase three-bridge-arm split capacitor topology, the mathematical model of the energy storage converter is: Among them, u jo represents the voltage of the midpoint of the jth bridge arm relative to the midpoint O of the DC bus capacitor, j = a, b, c, and the bridge arms a, b, c correspond to the phases A, B, and C of the distribution network one by one; e A 、e B 、e C Respectively represent the three-phase grid voltage of the distribution network; R LA , R LB , R LC Respectively represent the internal resistance of the three-phase filter inductor on the AC side; i aA 、i bB 、i cC They represent the current of the corresponding phase grid input into the midpoint of each bridge arm; L A , L B , L c They represent the inductance of the three-phase filter inductance on the AC side; C1 and C2 represent the capacity of the upper and lower bus capacitors on the DC side; U dc1 , U dc2 Respectively represent the terminal voltage of the upper and lower bus capacitors on the DC side; S j Indicates the switch state of the jth bridge arm; U dc Indicates the DC bus voltage.

3. The energy storage converter controller optimization method according to claim 1, characterized in that: The outer loop is a power outer loop or a voltage outer loop, and the outer loop adopts a PI controller.

4. The energy storage converter controller optimization method according to claim 1, characterized in that: The recursive least square method with forgetting factor is used to determine the recursive expression of the identification parameter; wherein the recursive expression of the identification parameter is: in, Represents θ km The recursive estimate of θ km represents the identification parameter matrix of the mth phase at the kth moment, Represents θ k-1,m The recursive estimate of θ k-1,m represents the identification parameter matrix of the mth phase at the k-1th moment, m = A, B, C; β km , H k-1,m , Both represent intermediate quantities; p km represents the input quantity of the mth phase at the kth moment; q km represents the output of the mth phase at the kth moment; λ represents the forgetting factor; p 1m Represents the input quantity of the mth phase at the first moment; the superscript T represents transposition.

5. The energy storage converter controller optimization method according to claim 1, characterized in that: The judgment condition for starting parameter identification calculation is: When 0≤I mse <δ, start parameter identification calculation; when I mse When ≥δ, parameter identification calculation is prohibited; Among them, δ represents the error threshold, I mse represents the real-time current tracking error of the deadbeat controller; the real-time current tracking error I mse for: in, represents the m-th phase command current of the energy storage converter at the k-th moment, n represents the number of sampling points in one power frequency cycle, n=f / 50, f represents the sampling frequency; i jm (k) represents the current input into the m-phase grid at the midpoint of the j-th bridge arm at the k-th moment, and the bridge arms a, b, and c correspond to the A, B, and C phases of the distribution network one by one.

6. The energy storage converter controller optimization method according to any one of claims 1 to 5, characterized in that: The convergence is determined based on the identification parameters at the current moment and the previous moment, and the convergence condition is: in, Represents θ km The recursive estimate of (i), θ km (i) represents the i-th parameter in the identification parameter matrix of the m-th phase at the k-th moment, Represents θ k-1,m The recursive estimate of (i), θ k-1,m (i) represents the i-th parameter in the identification parameter matrix of the m-th phase at the k-1-th time; T S represents the sampling period; ζ represents the convergence threshold.

7. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the energy storage converter controller optimization method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the energy storage converter controller optimization method according to any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the energy storage converter controller optimization method according to any one of claims 1 to 6 is implemented.

Images