A control method and system of a V2G charging pile based on a system impedance mapping mechanism

Abstract

The present application relates to a kind of control method and system of V2G charging pile based on system impedance mapping mechanism, method includes: respectively constructing the three-phase voltage equation of grid side, voltage equation under synchronous rotating coordinate system and vehicle side DC bus voltage equation, and real-time estimation grid impedance, based on the above results generation reference signal;Based on reference signal, utilize neural network output prediction sequence, and in the prediction time domain, prediction sequence is based on voltage adjustment weight and harmonic suppression weight optimization, obtain optimization control sequence;Actual DC bus voltage and actual harmonic current are obtained, and voltage adjustment weight and harmonic suppression weight are adjusted in combination with optimization control sequence, based on the harmonic suppression weight after adjustment and optimization control sequence generation switch signal, based on switch signal control V2G charging pile.Compared with prior art, the present application not only can give consideration to V2G charging pile charging and discharging when grid voltage is stable, also can better inhibit the harmonic generated in this process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging, in particular to a control method of a V2G charging pile based on a system impedance mapping mechanism. BACKGROUND

[0002] In recent years, electric vehicle (EV) technology has gradually matured, and charging piles, as the infrastructure of electric vehicles, have developed rapidly. The topology structure and power control strategy of charging piles have an increasingly significant impact on the power grid. In particular, when charging piles and the power grid perform bidirectional energy interaction, charging piles not only obtain electrical energy from the power grid but also feed back electrical energy to the power grid. This bidirectional energy interaction can cause low-frequency harmonics to appear in the power grid, and in a fusion power supply system, the emission and conduction mechanism of low-frequency harmonics is complex, which can easily cause resonance amplification and affect the stability of the power grid.

[0003] Currently, many control methods such as proportional-resonant control, adaptive filter control, and DC side voltage control have been proposed to address the problem of low-frequency harmonics. Compared with changing the topology structure, adjusting the control method to eliminate filtering has more advantages in response speed, grid adaptability, and iterative updating capability. Specifically, various control methods are introduced to filter out low-frequency harmonics. The proportional-resonant control has good filtering effect on harmonics of a specific frequency, but when there are multiple harmonic frequencies in the power grid, the anti-interference ability of the proportional-resonant controller is weak. The adaptive filter control has strong adaptability and can cope with changes in harmonics in the power grid, but when the harmonics in the power grid change greatly, the adaptive algorithm may not be able to adapt quickly, causing system instability. To solve the above problems, Chinese patent application CN118763703A discloses a control method for a V2G charging pile bidirectional AC / DC converter, which obtains electromagnetic power through a fuzzy PI controller, obtains a fundamental current reference value through a virtual synchronous machine algorithm, and calculates a compensation current reference value through a harmonic extraction algorithm. A model predictive controller based on a genetic algorithm is established to select the optimal switching tube breaking state and control the switching tubes of the three-phase bridge circuit. Although this method can filter out low-frequency harmonics caused by bidirectional energy interaction of the charging pile to some extent and reduce the impact on the stability of the power grid, it uses a fixed weight cost function as the fitness function of the genetic function when generating control switching using the genetic algorithm, ignoring the nonlinear dynamic impact of switching action on the power grid, which affects the quality of the generated control switching and further affects the stability of the power grid.

[0004] Therefore, it is a technical problem to provide a control method for a V2G charging pile that can consider the nonlinear dynamic impact of switching states on the power grid. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a control method and system of a V2G charging pile based on a system impedance mapping mechanism, considering the system impedance of the power grid when establishing a power grid side model and a vehicle side model and generating active power, reactive power and voltage reference signals; using a neural network predictive control method to predict and optimize the DC bus voltage, grid harmonic current and voltage control quantity; dynamically adjusting the voltage weight and harmonic weight to change the priority of control according to the error of the actual parameters and the prediction and the severity of the harmonics; based on the above and the adjusted weight, using a space vector modulation method to generate a switching signal for control.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] According to the first aspect of the present application, a control method of a V2G charging pile based on a system impedance mapping mechanism is provided, comprising:

[0008] The three-phase voltage equation of the power grid side, the voltage equation in the synchronous rotating coordinate system and the DC bus voltage equation of the vehicle side are respectively constructed, and the power grid impedance is estimated in real time, and the reference signal is generated based on the power grid impedance, the three-phase voltage equation of the power grid side, the voltage equation in the synchronous rotating coordinate system and the DC bus voltage equation of the vehicle side;

[0009] The neural network output prediction sequence is obtained based on the reference signal, and the prediction sequence is optimized in the prediction time domain based on the voltage regulation weight and the harmonic suppression weight to obtain the optimized control sequence; the prediction sequence includes the DC bus voltage, the harmonic current and the dq axis voltage control quantity;

[0010] The actual DC bus voltage and the actual harmonic current are obtained, and the voltage regulation weight and the harmonic suppression weight are adjusted in combination with the optimized control sequence, the switching signal is generated based on the adjusted harmonic suppression weight and the optimized control sequence, and the V2G charging pile is controlled based on the switching signal.

[0011] As a preferred technical solution, the method for constructing the three-phase voltage equation of the power grid side, the voltage equation in the synchronous rotating coordinate system and the DC bus voltage equation of the vehicle side comprises:

[0012] The actual data of the power grid is obtained, and the three-phase voltage equation of the power grid side is constructed in the DC coordinate system based on the actual data of the power grid, and the expression is:

[0013] ,

[0014] Among them, 、 and respectively represent the three-phase voltage of the power grid side. Indicates the equivalent resistance of the power grid; , as well as These represent the three-phase currents on the grid side; Indicates the equivalent inductance of the power grid; , as well as These represent the three-phase electromotive forces on the grid side;

[0015] Using coordinate transformation theory, the three-phase voltage equations in the DC coordinate system are transformed into voltage equations in the synchronous rotating coordinate system, and their expressions are as follows:

[0016] ,

[0017] in, and These represent the voltage components along the d-axis and q-axis in a synchronously rotating coordinate system, respectively. and These represent the current components along the d-axis and q-axis in a synchronously rotating coordinate system, respectively. and These represent the electromotive force components along the d-axis and q-axis in a synchronously rotating coordinate system, respectively.

[0018] Based on Kirchhoff's current law and the power balance principle, and using the actual power grid data, the DC bus voltage equation on the vehicle side is established, and its expression is:

[0019] ,

[0020] in, Indicates the DC bus voltage; Indicates the DC bus capacitance; This represents the DC current output by the AC-DC converter; This indicates the load current.

[0021] As a preferred technical solution, the method for estimating the power grid impedance is as follows: ,in, Indicates the equivalent resistance of the power grid; Represents the imaginary unit; It represents angular frequency.

[0022] As a preferred technical solution, the reference signal includes a grid-side active power reference signal, a grid-side reactive power reference signal, and a DC bus voltage reference signal. The method for generating the reference signal is as follows:

[0023] The active power reference signal and reactive power reference signal on the grid side are calculated based on the grid impedance and the voltage equation in the synchronous rotating coordinate system; the DC bus voltage reference signal is generated based on the DC bus voltage equation.

[0024] The expression for the reference signal is:

[0025] ,

[0026] in, This indicates the active power reference signal on the grid side; This represents the nth component of the active power reference signal; This indicates the reactive power reference signal on the grid side. This represents the nth component of the reactive power reference signal; This indicates the DC bus voltage reference signal; This represents the nth component of the DC bus voltage reference signal.

[0027] As a preferred technical solution, the method for obtaining the optimized control sequence includes:

[0028] Acquire historical state data of the power grid, and construct an input data vector based on the historical state data and reference signals;

[0029] The input data vector is input into a neural network, which outputs a prediction sequence, wherein the prediction sequence is:

[0030] ,

[0031] in, Represents the prediction of the k-th time based on the input data vector at time k. DC bus voltage at any given time; The output layer weight matrix represents the DC bus voltage; Indicates the neural network in the first... The hidden state at any given moment; The output layer bias term represents the DC bus voltage; Represents the prediction of the k-th time based on the input data vector at time k. Power grid harmonic current at any given moment; Output layer weight matrix of grid harmonic current; The output layer bias term represents the grid harmonic current. and Represents the prediction of the k-th time based on the input data vector at time k. Voltage control quantities on the d-axis and q-axis at any given time; The output layer weight matrix represents the voltage control quantities along the d-axis and q-axis; Output layer bias term representing the voltage control values ​​for the d-axis and q-axis;

[0032] The predicted sequence is optimized in the prediction time domain using a neural network predictive control method to generate an optimized control sequence, the expression of which is:

[0033] ,

[0034] in, Indicates the prediction time domain; The voltage regulation weight represents the DC bus voltage; Indicates a reference value for the DC bus voltage; Indicates harmonic suppression weight; Indicates control over the time domain; Indicates the first The rate of change of the control quantity at any given time.

[0035] As a preferred technical solution, the method for adjusting the voltage regulation weight and harmonic suppression weight is as follows:

[0036] ,

[0037] in, This represents the voltage regulation weight at time k; This indicates the preset voltage regulation reference weight; The adjustment ratio coefficient representing the voltage regulation weight; Let represent the error of the DC bus voltage at time k, and , This represents the actual DC bus voltage at time k. Indicates based on the first The DC bus voltage at time k is predicted from the input data vector at time k. The integral coefficient representing the adjustment of voltage regulation weight; Indicates the first Error of DC bus voltage at any given time; This represents the harmonic suppression weight at time k; This indicates the preset harmonic suppression benchmark weight; This represents the adjustment ratio coefficient for harmonic suppression weight; Let represent the error of the grid harmonic current at time k, and , Represents the actual harmonic current. Indicates based on the first The harmonic current at time k is predicted from the input data vector at time k. The integral coefficient representing the adjustment of the harmonic suppression weight; Indicates the first Error of harmonic current in the power grid at any given time.

[0038] The method for generating the switching signal is preferably as follows:

[0039] The optimized DC bus voltage, harmonic current and dq-axis voltage control quantity are obtained based on the optimized control sequence;

[0040] The composite vector is generated based on the optimized DC bus voltage, harmonic current and dq-axis voltage control quantity, and the adjusted voltage regulation weight and harmonic suppression weight, and the final reference signal is obtained by synthesizing the composite vector, and the expression is as follows:

[0041] ,

[0042] wherein, represents the final reference signal at the kth moment; represents the weight coefficient of the ith optimized quantity at the kth moment, and when i = 1, represents the voltage regulation weight, when i = 2, represents the harmonic suppression weight, when i = 3, represents the weight of the current component of the d-axis and q-axis; represents the ith optimized quantity at the kth moment, and when i = 1, represents the optimized DC bus voltage, when i = 2, represents the optimized harmonic current, when i = 3, represents the current component of the d-axis and q-axis; represents the output layer weight matrix of the voltage control quantity of the d-axis and q-axis; represents the hidden state of the neural network at the kth moment; The switching signal is generated by space vector modulation based on the final reference signal.

[0043] The method further comprises determining a reverse compensation current based on the prediction sequence, and performing grid harmonic suppression based on the reverse compensation current, and the calculation method of the reverse compensation current is as follows:

[0044] wherein, represents the reverse compensation current at the kth moment, represents the harmonic suppression weight, represents the grid harmonic current at the kth moment predicted based on the input data vector at the kth moment.

[0045] ​​​As a preferred technical solution, the method further comprises: calculating a feedback value based on the power grid actual output vector and the optimization control sequence at the corresponding moment, adjusting the voltage control amount of the d-axis and the q-axis in the future time domain based on the feedback value, and updating the model parameters of the neural network.

[0046] According to a second aspect of the present application, a control system of a V2G charging pile based on a system impedance mapping mechanism is provided for implementing the above method.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] 1) The traditional vector control adopts linear modeling in a rotating coordinate system, but there are discrete PWM switching characteristics in the actual system, for example, voltage jump caused by switching action, current distortion caused by dead zone effect, and the problem of affecting the stability of the power grid. The present application learns the nonlinear dynamic characteristics of the power grid including the DC bus voltage, harmonic current and dq-axis voltage component by using a neural network, considers the above complex factors when predicting, and uses a neural network predictive control method (NNPC) to perform rolling optimization on the generated prediction sequence. The model parameters, voltage adjustment weight and harmonic suppression weight are adjusted online to ensure that the generated optimization control sequence can adapt to the changes of the dead zone in real time, so that the switching quantity generated based on the optimization control variable can avoid current distortion caused by voltage jump and dead zone effect.

[0049] 2) The present application calculates the reverse compensation current based on the prediction sequence, dynamically suppresses the harmonics generated by the V2G charging pile in the power grid in real time based on the reverse compensation current, and ensures the stability of the power grid.

[0050] 3) The present application generates a plurality of sub-reference vectors based on the DC bus voltage, harmonic current and dq-axis voltage component, and introduces dynamic weights for priority allocation, solving the problem that the traditional V2G charging pile control problem is often controlled for a single target, and it is difficult to coordinate multi-objective optimization. DETAILED DESCRIPTION

[0051] Figure 1 The method flowchart of the present application;

[0052] Figure 2 The adjusted voltage result graph corresponding to embodiment 2 of the present application;

[0053] Figure 3 The harmonic suppression result graph corresponding to embodiment 3 of the present application. DETAILED DESCRIPTION

[0054] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0055] Unless otherwise defined, technical terms or scientific terms used in the present application should be understood as the common meanings thereof to those skilled in the art to which the present application pertains. The terms "one", "a", "an", "the", and similar terms in the present application do not denote a singular number or quantity but include a plural number or quantity unless otherwise defined by context. The terms "comprise", "comprising", "include", "including", "have", "has", "contain", "containing", or other similar terms in the present application are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a list of steps or units are not limited to the steps or units listed, but can further include other steps or units not listed or can further include other steps or units inherent to such process, method, product, or apparatus. The terms "connect", "connected", "coupling", and similar terms in the present application are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The term "multiple" in the present application refers to two or more. The term "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects. The terms "first", "second", "third", and the like in the present application are only to distinguish similar objects, and do not represent a specific order of the objects.

[0056] Embodiment 1

[0057] In view of the shortcomings of the prior art, the present application provides a control method for a V2G charging pile based on a system impedance mapping mechanism, which has the method flow as shown in Figure 1

[0058] In detail, the method comprises:

[0059] S1, a three-phase voltage equation of a grid side, a voltage equation in a synchronous rotating coordinate system, and a DC bus voltage equation of a vehicle side are respectively constructed, a grid impedance is estimated in real time, and a reference signal is generated based on the grid impedance, the three-phase voltage equation of the grid side, the voltage equation in the synchronous rotating coordinate system, and the DC bus voltage equation of the vehicle side.

[0060] S11, actual grid data is obtained, and a three-phase voltage equation of a grid side is constructed in a DC coordinate system based on the actual grid data, and the expression is: ​

[0061] ,

[0062] wherein, , and represent three-phase voltages at the grid side, respectively; represents the grid equivalent resistance; , and represent three-phase currents at the grid side, respectively; represents the grid equivalent inductance; , and represent three-phase electromotive forces at the grid side, respectively.

[0063] S12, by using the coordinate transformation theory, the three-phase voltage equation in the direct current coordinate system is converted to the voltage equation in the synchronous rotating coordinate system, and the expression is:

[0064] ,

[0065] wherein, and represent voltage components of the d-axis and the q-axis in the synchronous rotating coordinate system, respectively; and represent current components of the d-axis and the q-axis in the synchronous rotating coordinate system, respectively; and represent electromotive force components of the d-axis and the q-axis in the synchronous rotating coordinate system, respectively.

[0066] S13, based on the Kirchhoff's current law and the power balance principle, the direct current bus voltage equation at the vehicle side is established by using the actual data of the grid, and the expression is:

[0067] ,

[0068] wherein, represents the direct current bus voltage; represents the direct current bus capacitance; represents the direct current flowing out of the AC-DC converter; represents the load current.

[0069] S14, considering that the grid impedance will affect the dynamic characteristics of the system, the system impedance is considered for modeling in the application, and the influence of the grid impedance can be estimated or compensated in real time through the impedance mapping mechanism, and the specific grid impedance is wherein, represents the grid equivalent resistance; represents the imaginary unit; represents the angular frequency.

[0070] S15, reference signal generation.

[0071] The reference signal includes a grid-side active power reference signal, a grid-side reactive power reference signal, and a DC bus voltage reference signal.

[0072] The grid-side active power reference signal and the grid-side reactive power reference signal are calculated based on a grid impedance and a voltage equation in a synchronous rotating coordinate system; the DC bus voltage reference signal is generated based on a DC bus voltage equation, and its expression is:

[0073]

[0074] wherein, represents the grid-side active power reference signal; represents the nth component of the active power reference signal; represents the grid-side reactive power reference signal; represents the nth component of the reactive power reference signal; represents the DC bus voltage reference signal; represents the nth component of the DC bus voltage reference signal.

[0075] S2, output a prediction sequence based on the reference signal using a neural network, and optimize the prediction sequence based on a voltage adjustment weight and a harmonic suppression weight within a prediction time domain to obtain an optimized control sequence; the prediction sequence includes a DC bus voltage, a harmonic current, and a dq-axis voltage control amount.

[0076] S21, obtain historical state data of the grid, and construct an input data vector based on the historical state data and the reference signal wherein, k represents the kth moment.

[0077] S22, input the input data vector into the neural network, select LSTM as the prediction model in this embodiment, and output a prediction sequence, which is:

[0078]

[0079] wherein, represents the DC bus voltage at the kth moment predicted based on the input data vector; represents an output layer weight matrix of the DC bus voltage; represents the hidden state of the neural network at the kth moment; represents an output layer bias term of the DC bus voltage; represents the grid harmonic current at the kth moment predicted based on the input data vector; ​​​​​An output layer weight matrix of the grid harmonic current; An output layer bias term of the grid harmonic current; And The voltage control amount of the d-axis and the q-axis at the k+1 moment predicted based on the input data vector at the k moment; The voltage control amount of the d-axis and the q-axis at the k+1 moment predicted based on the input data vector at the k moment; An output layer weight matrix of the voltage control amount of the d-axis and the q-axis; An output layer bias term of the voltage control amount of the d-axis and the q-axis.

[0080] S23, determining a reverse compensation current based on the predicted sequence, and performing grid harmonic suppression based on the reverse compensation current, wherein the calculation method of the reverse compensation current is: Wherein, The reverse compensation current at the k+1 moment is represented as, The reverse compensation current at the k+1 moment is represented as, The harmonic suppression weight is represented as, The grid harmonic current at the k+1 moment predicted based on the input data vector at the k moment is represented as. The grid harmonic current at the k+1 moment predicted based on the input data vector at the k moment is represented as.

[0081] S24, rolling optimization of the predicted sequence in the prediction time domain by using the NNPC.

[0082] Considering that the traditional vector control adopts linear modeling in the rotating coordinate system, but there are discrete PWM switching characteristics in the actual system, for example, switching actions will cause voltage jumps, and dead-time effects will cause current distortion, the application utilizes LSTM to learn the nonlinear dynamic characteristics in the system, and complex factors such as the nonlinear dynamic characteristics of the DC bus voltage, harmonic current and dq-axis voltage components are fed to LSTM as training data, so that these nonlinear problems can be considered in the prediction process, and combined with the NNPC rolling optimization, the model parameters are adjusted online, which can adapt to the change of the dead zone in real time, so that the generated optimization control sequence can avoid the influence of the dead-time effect.

[0083] In detail, the method for generating the optimization control sequence is:

[0084] ,

[0085] Wherein, The prediction time domain is represented as; The voltage regulation weight of the DC bus voltage is represented as; The reference value of the DC bus voltage is represented as; The harmonic suppression weight is represented as; The control time domain is represented as; The control amount change rate at the k+1 moment is represented as. The control amount change rate at the k+1 moment is represented as.

[0086] The feedback value is calculated based on the actual output vector of the power grid and the optimized control sequence at the corresponding time. The voltage control quantities of the d-axis and q-axis in the future time domain are adjusted based on the feedback value, and the model parameters of the LSTM are updated to achieve rolling optimization.

[0087] The method for calculating the feedback value is as follows: , This represents the actual output vector at the current time k, which includes the DC bus voltage. Power grid harmonic current and d / q axis current and ; This represents the optimized control sequence at the corresponding time point.

[0088] S3. Obtain the actual DC bus voltage and actual harmonic current, and adjust the voltage regulation weight and harmonic suppression weight in combination with the optimized control sequence. Generate a switching signal based on the adjusted harmonic suppression weight and the optimized control sequence, and control the V2G charging pile based on the switching signal.

[0089] S31. Adjust the voltage regulation weight and harmonic suppression weight.

[0090] Specifically, the adjustment method is as follows:

[0091] ,

[0092] in, This represents the voltage regulation weight at time k; This indicates the preset voltage regulation reference weight; The adjustment ratio coefficient representing the voltage regulation weight; Let represent the error of the DC bus voltage at time k, and , This represents the actual DC bus voltage at time k. Indicates based on the first The DC bus voltage at time k is predicted from the input data vector at time k. The integral coefficient representing the adjustment of voltage regulation weight; Indicates the first Error of DC bus voltage at any given time; This represents the harmonic suppression weight at time k; This indicates the preset harmonic suppression benchmark weight; This represents the adjustment ratio coefficient for harmonic suppression weight; Let represent the error of the grid harmonic current at time k, and , Represents the actual harmonic current. Indicates based on the first The harmonic current at time k is predicted from the input data vector at time k. The integral coefficient representing the adjustment of the harmonic suppression weight; Indicates the first Error of harmonic current in the power grid at any given time.

[0093] S32, Generate switch signal.

[0094] S321. Obtain the optimized DC bus voltage, harmonic current and dq axis voltage control quantities based on the optimized control sequence;

[0095] S322. Based on the optimized DC bus voltage, harmonic current, and dq-axis voltage control quantities, as well as the adjusted voltage regulation weights and harmonic suppression weights, a composite vector is generated. The composite vector is then synthesized to obtain the final reference signal, the expression of which is:

[0096] ,

[0097] in, This represents the final reference signal at time k; Let represent the weight coefficient of the i-th optimization quantity at time k, and when i=1 This represents the voltage regulation weight when i=2. This represents the harmonic suppression weight, when i=3. Indicates the weights of the current components along the d-axis and q-axis; Let i represent the i-th optimization at time k, and when i=1 This represents the optimized DC bus voltage when i=2. This represents the optimized harmonic current when i=3. Represents the current components along the d-axis and q-axis; The output layer weight matrix represents the voltage control values ​​along the d-axis and q-axis; Indicates the neural network in the first... The hidden state at any given moment;

[0098] S323. Based on the final reference signal, perform space vector modulation to generate a switching signal, and use the switching signal to control the V2G charging pile.

[0099] Example 2

[0100] To verify the feasibility of the methods provided in the above embodiments, this embodiment applies the method provided by this invention to a V2G charging pile system for V2G charging pile control and voltage regulation and resonance suppression of the power grid, and plots voltage regulation diagrams and harmonic suppression diagrams, as shown below. Figure 2 and Figure 3 As shown.

[0101] Among them, the effect of voltage regulation is as followsFigure 2 As shown in the figure, it can be seen that the grid voltage is constant at 311V from 0s to 0.2s, and at 0.2s, the grid voltage suddenly drops to 100V, at the same time, the weight coefficient is rapidly increased from 0.1 to 0.5, at this time, the grid voltage regulation is preferentially performed; from 0.2s to 0.3s, it is the grid voltage recovery period, the weight coefficient is also gradually decreased from 0.5, at 0.3s, the grid voltage rises to 311V and the weight coefficient also decreases to 0.1, and the grid voltage regulation is completed.

[0102] The effect of resonance suppression is shown in the figure Figure 3 As shown in the figure, it can be seen that the current is a standard sine wave from 0s to 0.2s, and at 0.2s, a large amount of harmonics suddenly appears, under the influence of the harmonics, the current is Figure 3 represented by a black dashed line, at this time, the weight coefficient is rapidly increased from 0.1, at this time, the necessary harmonic treatment is preferentially performed, at 0.25s, the weight coefficient is increased to 0.9, and the treated current is Figure 3 represented by a black solid line, after 0.25s, the harmonic treatment is continuously performed, and the influence of the treated harmonics on the current is reduced. In summary, it can be seen that the method provided by the present application can effectively suppress the voltage fluctuation and resonance caused by the V2G charging pile during charging and discharging, and plays a good protection role for the stability of the power grid.

[0103] Embodiment 3

[0104] In addition, the present application also provides a control system of a V2G charging pile based on a system impedance mapping mechanism, which comprises a central processing unit (CPU), which can perform various appropriate actions and processing according to computer program instructions stored in a read-only memory (ROM) or loaded into a random access memory (RAM) from a storage unit. In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0105] A plurality of components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, speakers, etc.; a storage unit, such as a magnetic disk, an optical disk, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunications networks.

[0106] The processing units perform the various methods and processes described above, such as methods S1-S3. For example, in some embodiments, methods S1-S3 can be implemented as a computer software program tangibly embodied in a machine readable medium, such as a storage unit. In some embodiments, portions or all of the computer program can be loaded and / or installed onto the device via the ROM and / or the communication unit. When the computer program is loaded onto the RAM and executed by the CPU, one or more of the steps of methods S1-S3 described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform methods S1-S3 by other means, such as by way of firmware.

[0107] The functionality described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0108] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be retrieved from a machine-readable medium or device, a storage medium, a memory medium, a tangible medium, or a non-transitory medium. The program code can be executed by a machine, such as a computer, which can be a special purpose computer or a general purpose computer. The program code can be executed by a controller or a processor, which can be a special purpose controller or a general purpose controller.

[0109] In the context of the present application, a machine-readable medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of a computer program code, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements shall be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A control method for V2G charging piles based on system impedance mapping mechanism, characterized in that, include: The three-phase voltage equations on the grid side, the voltage equations in the synchronous rotating coordinate system, and the DC bus voltage equations on the vehicle side are constructed respectively. The grid impedance is estimated in real time, and a reference signal is generated based on the grid impedance, the three-phase voltage equations on the grid side, the voltage equations in the synchronous rotating coordinate system, and the DC bus voltage equations on the vehicle side. Based on the aforementioned reference signal, a neural network is used to output a prediction sequence, and the prediction sequence is optimized in the prediction time domain based on voltage regulation weights and harmonic suppression weights to obtain an optimized control sequence; the prediction sequence includes DC bus voltage, harmonic current, and dq axis voltage control quantities. The actual DC bus voltage and actual harmonic current are obtained, and the voltage regulation weight and harmonic suppression weight are adjusted in combination with the optimized control sequence. A switching signal is generated based on the adjusted harmonic suppression weight and the optimized control sequence, and the V2G charging pile is controlled based on the switching signal. The method for generating the aforementioned switching signal is as follows: The optimized DC bus voltage, harmonic current, and dq axis voltage control quantities are obtained based on the optimized control sequence. Based on the optimized DC bus voltage, harmonic current, and dq-axis voltage control quantities, as well as the adjusted voltage regulation weights and harmonic suppression weights, a composite vector is generated. This composite vector is then synthesized to obtain the final reference signal, the expression of which is: ， in, This represents the final reference signal at time k; Let represent the weight coefficient of the i-th optimization quantity at time k, and when i=1 This represents the voltage regulation weight when i=2. This represents the harmonic suppression weight, when i=3. Indicates the weights of the current components along the d-axis and q-axis; Let i represent the i-th optimization at time k, and when i=1 This represents the optimized DC bus voltage when i=2. This represents the optimized harmonic current when i=3. Represents the current components along the d-axis and q-axis; The output layer weight matrix represents the voltage control quantities along the d-axis and q-axis; Indicates the neural network in the first... The hidden state at any given moment; A switching signal is generated by space vector modulation based on the final reference signal; The method further includes: determining a reverse compensation current based on the predicted sequence, and performing grid harmonic suppression based on the reverse compensation current, wherein the calculation method for the reverse compensation current is as follows: ,in, Indicates the first Reverse compensation current at time, Indicates the harmonic suppression weight. Represents the prediction of the k-th time based on the input data vector at time k. The harmonic current of the power grid at any given time.

2. The control method for a V2G charging pile based on the system impedance mapping mechanism according to claim 1, characterized in that, The methods for constructing the three-phase voltage equations on the grid side, the voltage equations in the synchronous rotating coordinate system, and the DC bus voltage equations on the vehicle side include: Obtain actual power grid data, and based on this data, construct the three-phase voltage equations for the power grid side in a DC coordinate system. The expression is as follows: ， in, , as well as These represent the three-phase voltages on the grid side; Indicates the equivalent resistance of the power grid; , as well as These represent the three-phase currents on the grid side; Indicates the equivalent inductance of the power grid; , as well as These represent the three-phase electromotive forces on the grid side; Using coordinate transformation theory, the three-phase voltage equations in the DC coordinate system are transformed into voltage equations in the synchronous rotating coordinate system, and their expressions are as follows: ， in, and These represent the voltage components along the d-axis and q-axis in a synchronously rotating coordinate system, respectively. and These represent the current components along the d-axis and q-axis in a synchronously rotating coordinate system, respectively. and These represent the electromotive force components along the d-axis and q-axis in a synchronously rotating coordinate system, respectively. Based on Kirchhoff's current law and the power balance principle, and using the actual power grid data, the DC bus voltage equation on the vehicle side is established, and its expression is: ， in, Indicates the DC bus voltage; Indicates the DC bus capacitance; This represents the DC current output by the AC-DC converter; This indicates the load current.

3. The control method for a V2G charging pile based on the system impedance mapping mechanism according to claim 1, characterized in that, The method for estimating the aforementioned grid impedance is as follows: ,in, Indicates the equivalent resistance of the power grid; Represents the imaginary unit; It represents angular frequency.

4. The control method for a V2G charging pile based on the system impedance mapping mechanism according to claim 1, characterized in that, The reference signals include a grid-side active power reference signal, a grid-side reactive power reference signal, and a DC bus voltage reference signal. The method for generating the reference signals is as follows: The active power reference signal and reactive power reference signal on the grid side are calculated based on the grid impedance and the voltage equation in the synchronous rotating coordinate system; the DC bus voltage reference signal is generated based on the DC bus voltage equation. The expression for the reference signal is: ， in, This indicates the active power reference signal on the grid side; This represents the nth component of the active power reference signal; This indicates the reactive power reference signal on the grid side. This represents the nth component of the reactive power reference signal; This indicates the DC bus voltage reference signal; This represents the nth component of the DC bus voltage reference signal.

5. The control method for a V2G charging pile based on the system impedance mapping mechanism according to claim 1, characterized in that, The method for obtaining the optimized control sequence includes: Acquire historical state data of the power grid, and construct an input data vector based on the historical state data and reference signals; The input data vector is input into a neural network, which outputs a prediction sequence, wherein the prediction sequence is: ， in, Represents the prediction of the k-th time based on the input data vector at time k. DC bus voltage at any given time; The output layer weight matrix represents the DC bus voltage; Indicates the neural network in the first... The hidden state at any given moment; The output layer bias term represents the DC bus voltage; Represents the prediction of the k-th time based on the input data vector at time k. The harmonic current of the power grid at any given moment; Output layer weight matrix of grid harmonic current; The output layer bias term represents the grid harmonic current. and Represents the prediction of the k-th time based on the input data vector at time k. Voltage control quantities on the d-axis and q-axis at any given time; The output layer weight matrix represents the voltage control quantities along the d-axis and q-axis; Output layer bias term representing voltage control values ​​for the d-axis and q-axis; The predicted sequence is optimized in the prediction time domain using a neural network predictive control method to generate an optimized control sequence, the expression of which is: ， in, Indicates the prediction time domain; The voltage regulation weight represents the DC bus voltage; Indicates a reference value for the DC bus voltage; Indicates harmonic suppression weight; Indicates control over the time domain; Indicates the first The rate of change of the control quantity at any given time.

6. The control method for a V2G charging pile based on the system impedance mapping mechanism according to claim 1, characterized in that, The method for adjusting the voltage regulation weights and harmonic suppression weights is as follows: ， in, This represents the voltage regulation weight at time k; This indicates the preset voltage regulation reference weight; The adjustment ratio coefficient representing the voltage regulation weight; Let represent the error of the DC bus voltage at time k, and , This represents the actual DC bus voltage at time k. Indicates based on the first The DC bus voltage at time k is predicted from the input data vector at time k. The integral coefficient representing the adjustment of voltage regulation weight; Indicates the first Error of DC bus voltage at any given time; This represents the harmonic suppression weight at time k; This indicates the preset harmonic suppression benchmark weight; This represents the adjustment ratio coefficient for harmonic suppression weight; Let represent the error of the grid harmonic current at time k, and , Represents the actual harmonic current. Indicates based on the first The harmonic current at time k is predicted from the input data vector at time k. The integral coefficient representing the adjustment of the harmonic suppression weight; Indicates the first Error of power grid harmonic current at any given time.

7. The control method for a V2G charging pile based on the system impedance mapping mechanism according to claim 1, characterized in that, The method further includes: calculating feedback values ​​based on the actual output vector of the power grid and the optimized control sequence at the corresponding time, adjusting the voltage control quantities of the d-axis and q-axis in the future time domain based on the feedback values, and updating the model parameters of the neural network.

8. A control system for a V2G charging pile based on system impedance mapping mechanism, characterized in that, The system is used to implement the method as described in any one of claims 1 to 7.

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