Energy storage converter control method and system based on variable universe fuzzy PID

Abstract

The invention relates to an energy storage converter control method and system based on variable universe fuzzy PID. A double-second-order generalized integrator accurately tracks and locks a grid-connected voltage amplitude and a phase; the data acquisition module takes the deviation e and the deviation change rate ec as input signals; the control domain module obtains an input scaling factor alpha (x) and an output scaling factor beta (x) according to an input signal; and the fuzzy control module performs fuzzy domain processing on the input signal to obtain error values delta kp, delta ki and delta kd of PID parameters. And the PID control module adjusts the alternating current side power. Through the arrangement, the phase and the amplitude of the power grid are accurately locked by adopting the double-second-order generalized integrator phase-locked loop, and meanwhile, the output power of the energy storage system is quickly adjusted to tend to a stable value. And meanwhile, adaptive adjustment of PID parameters is realized by applying a fuzzy reasoning method, dynamic and stable control of the power grid in a complex environment is realized by using fewer parameters, and a constant-power discharge effect of the energy storage system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage converters, and in particular to a control method and system of an energy storage converter based on variable domain fuzzy PID. Background Art

[0002] Energy storage converter is an important device in the power energy storage system. It can operate in grid-connected, off-grid, and on / off-grid switching modes. Different modes have different control targets and control strategies, and the actual operating conditions are relatively complex.

[0003] In the prior art, the current control strategy and phase-locked loop of the energy storage converter are optimized to speed up the response speed of tracking the expected value of the current. At the same time, the AC side grid synchronization adopts the phase-locked loop technology based on the dual second-order generalized integrator to effectively suppress the interference of grid fluctuations and the harmonics of the grid-connected current. However, there is a problem that the PID parameter selection has no adaptive ability. When the external input source power fluctuates and the load changes, the energy storage converter will be affected to a certain extent. In view of the adaptive adjustment ability of PID parameters, a control strategy of energy storage converter based on fuzzy control is proposed. Through fuzzy control, PID adaptive parameter adjustment is realized, bus voltage fluctuations and grid-side current harmonic content are reduced, and the transient stability ability of the system is enhanced. However, due to the fixed fuzzy domain, there is still a certain steady-state error. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a control system for an energy storage converter based on a variable domain fuzzy PID, comprising: a double second-order generalized integrator that divides a phase-locked loop into two second-order links to accurately track and lock the amplitude and phase of the power grid;

[0005] The data acquisition module obtains the deviation e and the deviation change rate ec of the AC side power based on the power grid information detected by the double second-order generalized integrator, and uses the deviation e and the deviation change rate ec of the AC side power as the input signal of the variable universe fuzzy PID controller;

[0006] The control domain module obtains the input scaling factor α(k) and the output scaling factor β(x) according to the input signal;

[0007] The fuzzy control module receives the input scaling factor α(x) and the output scaling factor β(x) output by the control domain module and the input signal, and performs fuzzy domain processing on the input signal according to the fuzzy rule base to obtain the error value Δk of the PID parameter. p , Δk i and Δk d ;

[0008] The PID control module is based on the error value Δk of the PID parameters. p , Δk iand Δk d Adaptively adjust the AC side power.

[0009] Preferably, the fuzzy domain processing uses seven fuzzy subsets, namely, NB, NM, NS, ZO, PS, PM, and PB, to perform fuzzy domain calculation on the error values ​​of the PID parameters.

[0010] Preferably, in the selection of membership function in the fuzzy domain processing, Gaussian functions are used at both ends of all subsets and triangular functions are used in the middle.

[0011] Preferably, the fuzzy rule base includes the following rules for the fuzzy PID parameter Δk p , Δk i and Δk d The setting law is as follows:

[0012] When the error |e| is large, k should be increased p , reduce k d , improve the system response speed and reduce the error caused by differential oversaturation;

[0013] When the deviation |e| and the deviation change rate |ec| are moderate, k needs to be reduced p This reduces overshoot and increases system response speed. i , k d Set to a moderate value;

[0014] When the deviation |e| is very small, increase k appropriately p and k i , improve the stability of the control system, k d Select a moderate value to reduce system oscillation.

[0015] Preferably, the fuzzy control module includes a defuzzification processing module, which performs defuzzification processing on the error value of the PID parameter output by the fuzzy control module to obtain an error result Δk p , Δk i and Δk d for:

[0016]

[0017] Preferably, the input scaling factor α(x) and the output scaling factor β(x) are:

[0018]

[0019] Preferably, an energy storage converter control system based on variable universe fuzzy PID comprises: the energy storage converter control method based on variable universe fuzzy PID is used to implement the energy storage converter control method based on variable universe fuzzy PID;

[0020] The dual second-order generalized integrator divides the phase-locked loop into two second-order links to accurately track and lock the amplitude and phase of the power grid;

[0021] The data acquisition module obtains the deviation and deviation change rate of the AC side power based on the power grid information detected by the double second-order generalized integrator, and uses the deviation and deviation change rate of the AC side power as the input signal of the variable universe fuzzy PID controller;

[0022] The control domain module obtains the input scaling factor α(x) and the output scaling factor β(x) according to the input signal;

[0023] The fuzzy control module receives the input scaling factor α(x) and the output scaling factor β(x) output by the control domain module and the input signal, and performs fuzzy domain processing on the input signal according to the fuzzy rule base to obtain the error value Δk of the PID parameter. p , Δk i and Δk d ;

[0024] PID control module, according to the error value Δk of PID parameters p , Δk i and Δk d Adaptively adjust the AC side power.

[0025] The beneficial effects of the present invention are as follows: a dual second-order generalized integrator is used as a loop filter to accurately track and lock the amplitude and phase of the power grid, and at the same time, the output power of the energy storage system is quickly adjusted and tends to a stable value; a fuzzy reasoning method is applied to realize adaptive adjustment of PID parameters, and dynamic stability control of the complex power grid environment is achieved with fewer parameters. At the same time, the fuzzy domain range is changed by using a telescoping factor, so that the fuzzy PID parameter values ​​are adjusted in real time, and a certain steady-state error is reduced, which helps to improve the accuracy and stability of AC side power control and achieve a constant power discharge effect of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0027] Figure 1 It is the main circuit topology diagram of the energy storage converter of the present invention;

[0028] Figure 2It is a block diagram of the variable domain fuzzy PID control system of the present invention;

[0029] Figure 3 It is a simulation model diagram of the energy storage converter of the present invention;

[0030] Figure 4 The block diagram of the dual second-order generalized integrator phase-locked loop system of the present invention;

[0031] Figure 5 It is a diagram of the DSOGI-PLL and SRF-PLL tracking the grid angular frequency of the present invention;

[0032] Figure 6 It is the block diagram of variable universe fuzzy control strategy;

[0033] Figure 7 It is the simulation waveform diagram of the present invention;

[0034] Figure 8 It is the FFT analysis diagram of the grid-connected current of the present invention;

[0035] Fig. 9 This is a diagram of a miniature physical platform of an energy storage converter of the present invention;

[0036] Fig.10 This is a waveform diagram of the AC side output voltage and current of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0039] The directions and positions mentioned in the present invention, such as "upper", "lower", "front", "back", "left", "right", "inside", "outside", "top", "bottom", "side", etc., are only for reference to the directions or positions of the drawings. Therefore, the directions and positions used are for explaining and understanding the present invention, but not for limiting the scope of protection of the present invention.

[0040] like Figures 1 to 10 As shown, it is an embodiment of the present invention, including: a double second-order generalized integrator divides the phase-locked loop into two second-order links, and accurately tracks and locks the amplitude and phase of the power grid;

[0041] The data acquisition module obtains the deviation e and the deviation change rate ec of the AC side power based on the power grid information detected by the double second-order generalized integrator, and uses the deviation e and the deviation change rate ec of the AC side power as the input signal of the variable universe fuzzy PID controller;

[0042] The control domain module obtains the input scaling factor α(x) and the output scaling factor β(x) according to the input signal;

[0043] The fuzzy control module receives the input scaling factor α(x) and the output scaling factor β(x) output by the control domain module and the input signal, and performs fuzzy domain processing on the input signal according to the fuzzy rule base to obtain the error value Δk of the PID parameter. p , Δk i and Δk d .

[0044] The PID control module is based on the error value Δk of the PID parameters. p , Δk i and Δk d Adjust the power on the AC side.

[0045] It can be understood that the energy storage converter realizes energy transfer between the energy storage medium and the power grid through a bidirectional DC / AC bridge, realizing bidirectional energy flow. When the system is discharging, the DC power output by the energy storage medium is inverted into AC power and transmitted to the power grid; when the system is charging, the AC power of the power grid is rectified into DC power and stored in the energy storage medium. The conventional two-level unipolar energy storage converter topology is as follows: Figure 1 As shown, S a1 ~S c2 is a switching device; u ob 、u ob 、u oc , e a 、e b 、e c are the phase voltage on the bridge arm side and the three-phase voltage on the grid side respectively; i a 、i b 、i c ,i ga 、i gb 、i gc They are three-phase inductor current and grid-side three-phase current respectively; L a , L b , L c , R d , C a , C b , C c They are filter inductance, filter inductance equivalent resistance, and filter capacitance respectively; U dc is the DC bus voltage, C1 and C2 are the DC bus capacitors.

[0046] In order to ensure the accuracy of the energy storage converter model, a switching function model needs to be established. The three-phase voltage of the power grid is assumed to be:

[0047]

[0048] In the formula, E g ,ω g are the phase voltage peak value and angular frequency of the power grid respectively.

[0049] According to the circuit principle, the AC side voltage loop equation is:

[0050]

[0051] Use switching functions to describe the states of complementary switching devices:

[0052]

[0053] In the formula, S k =1 means the upper bridge arm is turned on and the lower bridge arm is turned off; S k = -1, the opposite is true. Therefore, U dc , S k Indicates u aO 、u bO 、u cO :

[0054]

[0055] Substituting formula (4) into formula (2), we can obtain:

[0056]

[0057] In a three-phase three-wire system and when the grid is balanced,

[0058]

[0059] Substituting equation (6) into equation (5), the mathematical model in the abc stationary coordinate system is obtained as follows:

[0060]

[0061] Variable universe fuzzy PID control introduces a variable universe strategy, which adjusts the fuzzy universe to adapt to the changes of input signals and solves the problem of complex parameter adjustment. Figure 2 As shown, the control system adjusts the scaling factor according to the feedback error and the rate of change, and adjusts the fuzzy domain in real time; in this embodiment, the control domain module is Figure 2 The expansion factor in is the fuzzy control module, the fuzzy controller is the fuzzy control module, and the PID control module is the PID controller.

[0062] The fuzzy domain processing uses NB, NM, NS, ZO, PS, PM, and PB7 fuzzy subsets to perform fuzzy domain calculation on the error values ​​of PID parameters; in the selection of membership function, the fuzzy domain processing uses Gaussian functions at both ends of all subsets and a triangular function in the middle.

[0063] It can be understood that the fuzzy controller input selects the deviation e and the deviation change rate ec of the controlled object, processes the input through the fuzzy rule base, and outputs the three parameter corrections Δk of the PID controller p , Δk i and Δk d This paper uses 7 fuzzy subsets, NB, NM, NS, ZO, PS, PM, and PB, to calculate the fuzzy domain of each variable. In the selection of membership function, Gaussian functions are used at both ends of all subsets and triangular functions are used in the middle. The fuzzy set domain of input variables e and ec is [-5, 5], and the output variable Δk p , Δk i , Δk d The domain of fuzzy sets is [-1, 1].

[0064] The fuzzy rule base includes the fuzzy PID parameter Δk p , Δk i and Δk d The setting law is as follows:

[0065] When the error |e| is large, k should be increased p , reduce k d , improve the system response speed and reduce the error caused by differential oversaturation;

[0066] When the deviation |e| and the deviation change rate |ec| are moderate, k needs to be reduced p This reduces overshoot and increases system response speed. i , k d Set to a moderate value;

[0067] When the deviation |e| is very small, increase k appropriately p and k i , improve the stability of the control system, k d Select a moderate value to reduce system oscillation.

[0068] According to the above rules, 49 Δk p , Δk i and Δk d The fuzzy rules are shown in Tables 1, 2 and 3. The relationship between the PID 3 parameter correction values ​​and the error and error change rate is as follows:

[0069] Table 1 Δk pFuzzy rules

[0070]

[0071] Table 2 Δk i Fuzzy rules

[0072]

[0073] Table 3 Δk d Fuzzy rules

[0074]

[0075] The fuzzy control module includes a defuzzification processing module, which performs defuzzification processing on the error value of the PID parameter output by the fuzzy control module to obtain an error result Δk p , Δk i and Δk d for:

[0076]

[0077] It is understandable that conventional fuzzy PID controllers have a fixed domain range and are difficult to adapt to changes in the environment. When the control system exceeds the basic domain, it is difficult to maintain high-precision control, and may even cause the system to lose control. The variable domain fuzzy PID controller introduces a scaling factor based on fuzzy control to adjust the fuzzy domain range in real time, and uses the rule base more efficiently by adjusting the domain. The basic domain of input variables and output variables is

[0078]

[0079] In the formula, α(x) and β(x) are the scaling factors of the input and output variables, the initial domain of the input variable is [-E,E], and the initial domain of the output variable is [-EC,EC]. In this paper, the scaling factor of the input and output variables is designed based on the function.

[0080]

[0081] In the formula, 1<λ<0 reflects the accuracy of the control system, and the value of k reflects the sensitivity of the control system. The larger the value of k, the faster the change of the domain. This paper selects k=0.5 and λ=0.315 according to the controlled object. i (τ) is the input variable error, p i is the input variable weight coefficient, β(0) is the initial value of the output domain scaling factor, which is set to 0. After many experimental attempts, this paper selects the input and output scaling factors as shown in formula (11).

[0082]

[0083] The method comprises: the energy storage converter control method based on variable universe fuzzy PID is used to implement the energy storage converter control method based on variable universe fuzzy PID;

[0084] The dual second-order generalized integrator is used to divide the phase-locked loop into two second-order links to accurately track and lock the grid voltage amplitude and phase;

[0085] A data acquisition module is used to obtain the deviation e and the deviation change rate ec of the AC side power, and use the deviation e and the deviation change rate ec of the AC side power as input signals of the variable domain fuzzy PID controller;

[0086] A control domain module, used for obtaining an input scaling factor α(x) and an output scaling factor β(x) according to an input signal;

[0087] The fuzzy control module receives the input scaling factor α(x) and the output scaling factor β(x) output by the control domain module and the input signal, and performs fuzzy domain processing on the input signal according to the fuzzy rule base to obtain the error value Δk of the PID parameter. p , Δk i and Δk d ;

[0088] PID control module, according to the error value Δk of PID parameters p , Δk i and Δk d Adjust the power on the AC side.

[0089] The following energy storage converter simulation case established using Matlab / Simulink software is used to illustrate the feasibility of the implementation plan.

[0090] A simulation model of the energy storage system is established based on the mathematical model of the energy storage converter. Figure 3 As shown, the DC side energy storage battery is connected to one side of the DC / AC converter in parallel with the bus capacitor, and the other side is connected to the grid through the LC filter circuit and the transformer. An experimental platform is built in the laboratory environment, where the DC side battery voltage is set to 72V, the DC bus capacitor C1 = 1000μF, the AC side filter inductor L1 = 3mH, the AC side filter capacitor C2 = 4μF, and the AC side is connected to the grid through a 1:10 transformer.

[0091] like Figure 4As shown in the figure, the DSOGI-PLL used in this paper is a phase-locked loop method based on a dual second-order generalized integrator, which can be applied to non-ideal power grids. DSOGI-PLL is an improved PLL design method. Compared with the traditional SRF-PLL, DSOGI uses a dual second-order generalized integrator as a loop filter, divides the phase-locked loop into two second-order links, and can perform more accurate phase tracking and adjustment of the input signal. By introducing a generalized integrator, DSOGI-PLL can improve the anti-interference ability and frequency tracking range of the phase-locked loop, thereby effectively dealing with problems such as power grid fluctuations and harmonic distortion.

[0092] When the power grid fluctuates, the traditional SRF-PLL cannot accurately detect information such as the grid phase, frequency, and amplitude, which introduces harmonics into the grid-connected current. In contrast, the DSOGI-PLL can accurately detect the frequency and phase information of the grid voltage when the grid is unbalanced, and its dynamic response has almost no overshoot when the frequency changes suddenly. Figure 5 As shown in the figure, when the grid unbalance is introduced at 0.5 seconds and 1 second, the SRF-PLL detects the grid information with deviation, while the DSOGI-PLL can accurately detect the grid information.

[0093] The control strategy of the energy storage converter is as follows: Figure 6 As shown in the figure, the AC power outer loop and the current inner loop control are adopted, and the target control object is the AC side power, so that the energy storage system can achieve the effect of constant power discharge. The function of the power outer loop is to adjust the AC side power. The introduction of AC side power feedback and the use of variable domain fuzzy PID controller can achieve zero static error control of the AC side power. The output of the AC side power outer loop PID regulator is the current reference value i of the current inner loop. dref with i qref , thereby adjusting the active power output by the energy storage converter.

[0094] Energy storage converter output current i a ,i b ,i c After coordinate transformation, it is converted into i in dq coordinate system d ,i q . Then compare it with the current reference value i dref ,i qref By comparison, the PID regulator is used to adjust the d ,i q The output signal is controlled by After the inverse transformation, the drive signal of the energy storage converter can be obtained through SPWM, thereby realizing the grid-connected control of the energy storage system. In this paper, the initial active power P = 500W, the reactive power Q = 0Var, the active power setting value P = 700W is changed at 0.1 seconds, and the energy storage converter is in the unity power factor state, and only transmits active power to the grid.

[0095] The simulation results are as follows Figure 7 As shown, the green line is the traditional PID output result, the red line is the conventional fuzzy PID output result, and the blue line is the variable universe fuzzy PID output result. In terms of active power tracking, both the traditional PID control and the conventional fuzzy PID control have overshoot and a long adjustment time. When the variable universe fuzzy PID control is used, the simulation results have no obvious overshoot and quickly reach a steady state, and the active power tracking tends to be stable faster. In terms of reactive power tracking, both the traditional PID control and the conventional fuzzy PID control have the problem of large reactive power fluctuations. When the variable universe fuzzy PID control is used, the reactive power can quickly stabilize to 0, so that the system is in a unit power factor operation state. In terms of tracking speed and stability, the variable universe fuzzy PID control is superior to the traditional PID control and the conventional fuzzy PID control.

[0096] In order to compare the control effect in more detail, FFT analysis is performed on the simulated waveform of the grid-connected current output by the energy storage converter. Figure 8 (a) shows the FFT analysis of the output grid-connected current when the control strategy adopts fuzzy PID control. Figure 8 (b) shows the FFT analysis of the output grid-connected current when the control strategy adopts variable universe fuzzy PID control. It can be seen that when the control strategy adopts variable universe fuzzy PID control, the grid-connected current THD is reduced from 1.11% to 0.78%, and all odd harmonics are reduced.

[0097] According to the above topological structure, an experimental prototype was made. The actual test results are as follows: Fig. 9 As shown in the figure, the DC source is IT6723H DC regulated power supply, the RIGOL DS1104 oscilloscope records the output waveform frequency, and the Fluke 43B power quality analyzer records the output current THD. The AC side output voltage and current waveforms using the traditional PID algorithm and the algorithm proposed in this paper are shown in the figure. Fig.10 shown.

[0098] Fig.10 The blue line in the middle is the voltage waveform, and the yellow line is the current waveform. Under the fuzzy PID algorithm based on the synchronous coordinate system phase-locked loop, the THD of the AC side current is large and the frequency fluctuates. The algorithm proposed in this paper reduces the THD of the AC side current to 1.8%, effectively improves the AC side current waveform, and the frequency is stabilized at 50±0.1Hz, meeting the application scenario of the new power system grid-connected control.

[0099] This embodiment analyzes the main circuit topology, circuit model and working principle of the energy storage converter, and mathematically models the switching function of the energy storage converter. A variable domain fuzzy PID control is designed, and the voltage and phase of the power grid are locked based on a dual second-order generalized integral phase-locked loop to quickly track the active and reactive power output of the energy storage system. The energy storage converter model and algorithm are verified through simulation and experiments, and the conclusions are as follows:

[0100] (1) The variable domain fuzzy PID control can track active power and reactive power faster than traditional PID and conventional fuzzy PID control, and has a smaller overshoot.

[0101] (2) The dual second-order generalized integral phase-locked loop has a higher locking capability for grid voltage and phase than the traditional synchronous frame phase-locked loop. When the frequency suddenly changes, its dynamic response has almost no overshoot and can accurately detect information such as grid phase, frequency and amplitude.

[0102] (3) The control strategy proposed in this paper is implemented through an experimental prototype to verify the feasibility of the algorithm in this paper, which can effectively improve the grid-connected current quality and enhance the system's anti-interference ability.

[0103] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, CD-ROM, etc.

[0104] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

[0105] It should be noted that the embodiments of the present invention can be implemented by hardware, software or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in processor control codes, such as such codes provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0106] In addition, although the operation of the method of the present invention is described in a particular order in the accompanying drawings, this does not require or imply that these operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps described in the flow chart can change the order of execution. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution. It should also be noted that the features and functions of two or more devices according to the present invention can be embodied in one device. On the contrary, the features and functions of a device described above can be further divided into being embodied by multiple devices.

[0107] Although the invention has been described with reference to several specific embodiments, it should be understood that the invention is not limited to the specific embodiments disclosed. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A control method for energy storage converter based on variable universe fuzzy PID, characterized in that: include: The dual second-order generalized integrator divides the phase-locked loop into two second-order links to accurately track and lock the amplitude and phase of the power grid; The data acquisition module obtains the deviation e and the deviation change rate ec of the AC side power based on the power grid information detected by the double second-order generalized integrator, and uses the deviation e and the deviation change rate ec as the input signal of the variable universe fuzzy PID controller; The control domain module obtains the input scaling factor α(x) and the output scaling factor β(x) according to the input signal; The fuzzy control module receives the input scaling factor α(x) and the output scaling factor β(x) output by the control domain module and the input signal, and performs fuzzy domain processing on the input signal according to the fuzzy rule base to obtain the error value Δk of the PID parameter. p , Δk i and Δk d ; The PID control module is based on the error value Δk of the PID parameters. p , Δk i and Δk d Adaptively adjust the AC side power.

2. The energy storage converter control method based on variable universe fuzzy PID according to claim 1 is characterized in that: The fuzzy control module performs fuzzy domain processing on the input signal according to the fuzzy rule base. The fuzzy domain processing uses 7 fuzzy subsets of NB, NM, NS, ZO, PS, PM, and PB to perform fuzzy domain calculation on the error values ​​of the PID parameters.

3. The energy storage converter control method based on variable universe fuzzy PID according to claim 2 is characterized in that: In the selection of membership function for the fuzzy domain processing, Gaussian functions are used at both ends of all subsets and triangular functions are used in the middle.

4. The energy storage converter control method based on variable universe fuzzy PID according to claim 1 is characterized in that: The fuzzy control module performs fuzzy domain processing on the input signal according to the fuzzy rule base, wherein the fuzzy rule base includes the PID parameter error value Δk p , Δk i and Δk d The setting law is as follows: When the error |e| is large, k should be increased p , reduce k d , improve the system response speed and reduce the error caused by differential oversaturation; When the deviation |e| and the deviation change rate |ec| are moderate, k needs to be reduced p This reduces overshoot and increases system response speed, k i , k d Set to a moderate value; When the deviation |e| is very small, increase k appropriately p and k i , improve the stability of the control system, k d Select a moderate value to reduce system oscillation.

5. The energy storage converter control method based on variable universe fuzzy PID according to claim 1 is characterized in that: The fuzzy control module includes a defuzzification processing module, which performs defuzzification processing on the error value of the PID parameter output by the fuzzy control module to obtain an error result Δk p , Δk i and Δk d for 6. The energy storage converter control method based on variable universe fuzzy PID according to claim 1 is characterized in that: The input scaling factor α(x) and the output scaling factor β(x) are 7. A control system for energy storage converter based on variable universe fuzzy PID, characterized in that: include: The energy storage converter control system based on variable universe fuzzy PID is used to implement the energy storage converter control method based on variable universe fuzzy PID as described in any one of claims 1 to 6; The dual second-order generalized integrator divides the phase-locked loop into two second-order links to accurately track and lock the amplitude and phase of the power grid; The data acquisition module obtains the deviation e and the deviation change rate ec of the AC side power based on the power grid information detected by the double second-order generalized integrator, and uses the deviation e and the deviation change rate ec as the input signal of the variable universe fuzzy PID controller; The control domain module obtains the input scaling factor α(k) and the output scaling factor β(x) according to the input signal; The fuzzy control module receives the input scaling factor α(x) and the output scaling factor β(x) output by the control domain module and the input signal, and performs fuzzy domain processing on the input signal according to the fuzzy rule base to obtain the error value Δk of the PID parameter. p , Δk i and Δk d ; PID control module, according to the error value Δk of PID parameters p , Δk i and Δk d Adaptively adjust the AC side power.

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