CN116914829A - Fractional order self-adaption-based micro-grid VSG control method - Google Patents
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Abstract
The invention provides a fractional order self-adaption-based micro-grid VSG control method, which comprises the following steps: s101, establishing a circuit topology structure of a voltage sag generator (Voltage Sag Generator, VSG); s102, modeling a virtual speed regulator and a virtual excitation system of the VSG; s103, determining an initial value of the moment of inertia and an initial value of a damping coefficient of the virtual speed regulator through a machine learning algorithm; s104, designing an adaptive rate based on an initial value of rotational inertia and an initial value of a damping coefficient of a virtual speed regulator, and generating a VSG control strategy based on fractional order self-adaptation.
Description
Technical Field
The invention relates to the technical field of micro-grid operation control strategies, in particular to a micro-grid VSG control method based on fractional order self-adaption.
Background
The sea resources of China are rich, the islands are rich in new energy resources such as solar energy, wind energy and the like, so that in the environment of the tropical islands, the clean energy is reasonably utilized, and the utilization rate of the clean energy in a distributed power generation system is improved. In addition, the island environment has the defects of serious environmental pollution, bad weather and the like, so that the reliability and the stability of the power supply system are poor, and the system is required to have enough anti-interference capability. Distributed power supplies are often connected to a power grid through power electronics, but the power electronics do not have moment of inertia and damping coefficients, and as the proportion of the power supplies in the power grid increases, the inertia and damping of the system correspondingly decrease. When a system fails, the system is often unstable and even collapses due to insufficient inertia and damping, which is not in line with the conditions that the island-in-sea microgrid should meet. Therefore, in the island micro-grid, sufficient inertia and damping coefficient can be provided for the system by introducing the virtual synchronous generator VSG technology, so that the system has certain anti-interference capability, and a large number of micro-sources are integrated into the grid through the inverter in actual application. When a plurality of micro sources are connected in parallel, in a VSG parallel system, damping coefficients and rotational inertia can influence angular speed when power fluctuates. Due to the mutual coupling of the two VSGs, when their respective angular frequency increases deviate somewhat, they will cause a change in their active power increase, which will cause oscillations in the system.
In the VSG control system, the system controller cannot be simply set as a proportional integral controller due to its characteristics of nonlinearity, variable parameters, and susceptibility to external disturbance. Conventionally, the PI control algorithm is a method for controlling deviation based on feedback, and when external disturbance comes, although PI control can finally realize error control on controlled quantity, it is usually accompanied by large overshoot and oscillation. In order to restrain system oscillation, the invention designs a fractional order self-adaption-based micro-grid VSG control method. The control method improves the fitting degree of the system in different scenes, and can obtain good optimization effect in complex application scenes or aiming at complex controlled objects.
Disclosure of Invention
The invention aims to provide a fractional order self-adaptive micro-grid VSG control method to solve the problem of active power oscillation caused by parallel coupling of a plurality of VSGs.
In order to achieve the above object, the present invention provides a fractional order self-adaptive micro grid VSG control method, which includes the following steps:
s101, establishing a circuit topology structure of a voltage sag generator (Voltage Sag Generator, VSG);
s102, modeling a virtual speed regulator and a virtual excitation system of the VSG;
s103, determining an initial value of the moment of inertia and an initial value of a damping coefficient of the virtual speed regulator through a machine learning algorithm;
s104, designing an adaptive rate based on the initial value of the rotational inertia and the initial value of the damping coefficient of the virtual speed regulator, and generating a VSG control strategy based on fractional order adaptation.
Further, modeling is performed on a virtual speed regulator and a virtual excitation system of the VSG, specifically:
in a virtual governor, the angular expression of the VSG is:
wherein P is ref For reference rated power, P e For electromagnetic power omega n For rated angular frequency, J is rotational inertia, D is damping coefficient;
the virtual excitation system of the VSG is expressed as:
e is the amplitude of the output voltage of the inverter bridge arm of the VSG; k (K) q The voltage regulation coefficient of VSG is the voltage regulation coefficient reflecting the voltage regulation capability of the virtual excitation controller; q (Q) ref A set point representing a reference reactive power of the VSG; q represents the reactive power actually output by the VSG; d (D) q The droop coefficient representing the adjustment coefficient of reactive power, i.e. reactive power and voltage; u represents the actual voltage amplitude of the VSG output; u (U) ref Representing the magnitude of the reference voltage output by the VSG.
Further, in step S3, the initial moment of inertia J in the VSG is determined by a reinforcement learning Q-learning algorithm 0 And damping coefficient initial value D 0 。
Further, Q-learning algorithm to determine initial moment of inertia J in VSG 0 And damping coefficient initial value D 0 The method specifically comprises the following steps:
s201, inputting iteration times T, an action space A, a state space S, an exploration rate E, an attenuation coefficient gamma and an updating step length alpha, and outputting an action cost function Q (S, a), wherein S is a state, and a is an action;
s202, randomly initializing an action cost function Q (S, a), and setting the Q value of a termination state to be 0;
s203, initializing S as the first state of the current sequence;
s204, selecting a corresponding action a from the state S by adopting an E-greedy method;
s205, the agent interacts with the environment, and executes the selected action a to obtain a new state S' and a corresponding reward R;
s206, updating the action cost function Q (S, a) by using a time sequence difference method:
Q(s,a)＝(s,a)+[R t +γ*max α Q(s′,α)-Q(s,a)]；
s207, updating the state, wherein s=s';
s208, if S' meets the stop condition, completing the iteration of the current round, otherwise, turning to step S204;
s209, when the action cost function Q (S, a) converges, selecting corresponding action a in different initial value states S for different VSG systems, so as to determine the initial value of the moment of inertia and the initial value of the damping coefficient of the VSG system.
Further, the initial moment of inertia J is determined in the VSG by the reinforcement learning Q-learning algorithm 0 And damping coefficient initial value D 0 When the state set of the reinforcement learning Q-learning algorithm is set as the overshoot sigma of the system, the action set is set as the values of the adjustment moment of inertia J and the damping coefficient D, and the reward function is set as shown in the following formula:
wherein R is t As a reward function, sigma% is overshoot, t r Is the rise time.
Further, the self-adaptive rate is designed, and the VSG control strategy based on the fractional order self-adaptation is generated, and the method specifically comprises the following steps:
s301, analyzing a rotor angular frequency oscillation curve graph to divide an oscillation period into t 1 To t 2 、t 2 To t 3 、t 3 To t 4 、t 4 To t 5 Four time periods;
s302, analyzing the oscillation process in four time periods, and respectively constructing functional relations between rotation inertia and angular frequency change rate, damping coefficient and angular frequency increment in corresponding time periods;
s303, respectively constructing a damping coefficient D and a self-adaptive formula of the moment of inertia J based on fractional order according to the function relation constructed in the previous step.
Further, the adaptive formula of the damping coefficient D is as follows:
the adaptive formula for moment of inertia J is as follows:
wherein J is 0 And D 0 The moment of inertia and the damping coefficient of the system in steady state are respectively; k (K) J And K D Adaptive coefficients representing the moment of inertia and damping coefficient, respectively; n (N) 1 For adjusting threshold value of angular frequency variation, N 2 Is a threshold value for the amount of angular frequency deviation.
Compared with the prior art, the invention has the beneficial effects that:
compared with a VSG parallel system with fixed parameters, the micro-grid VSG control method based on fractional order self-adaption provided by the invention can eliminate the coupling between the systems to a certain extent, reduce the oscillation of active power and improve the dynamic performance of the systemDetermining initial values of moment of inertia and damping coefficient in adaptive control by reinforcement learning Q-lerning algorithm, and setting unique reward function, state set and action set in algorithm, which enables J in different VSG control systems 0 And D 0 The value of (2) is not a fixed value, and the initial value is set to be more fit with the characteristics of the system, so that the accuracy and the rapidity of self-adaptive control are improved. Compared with integer order self-adaptive control, the method improves the steady state and dynamic performance of the parallel system and improves the control precision. The controller developed and designed by adopting fractional order system theory has better robustness and control precision than the controller designed by adopting integer order system theory with traditional meaning. In addition, the fractional order self-adaptive controller is more in line with the actual control system in the current industry, and has better floor property.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only preferred embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic overall flow diagram of a micro-grid VSG control method based on fractional order adaptation according to an embodiment of the present invention.
Fig. 2 is a circuit topology diagram of a parallel VSG system provided by an embodiment of the present invention.
Fig. 3 is a graph of rotor angular frequency oscillations provided by an embodiment of the present invention.
Fig. 4 is a graph of an adaptive adjustment of damping coefficient D provided by an embodiment of the present invention.
Fig. 5 is a graph of an adaptive adjustment of moment of inertia J provided by an embodiment of the present invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the illustrated embodiments are provided for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Referring to fig. 1, the present embodiment provides a fractional order adaptive-based micro grid VSG control method, which includes the following steps:
and S101, establishing a circuit topology structure of the voltage sag generator (Voltage Sag Generator, VSG). Referring to fig. 2, the essence of VSG control is to embed a VSG control algorithm into the control algorithm of the inverter, which is centered on the design of the virtual governor and virtual excitation controller. In the circuit, a distributed power supply and energy storage are regarded as energy supply at a direct current side, and a large capacitor with a voltage stabilizing function is connected in parallel at the direct current side and then connected into a system through an inverter.
S102, modeling a virtual speed regulator and a virtual excitation system of the VSG.
The embodiment simulates a rotor motion equation and an excitation system of a synchronous generator, and designs a virtual speed regulator and a virtual excitation system of a VSG. The virtual speed regulator is a core in VGS control, and introduces rotational inertia j and damping coefficient D to enable the inverter to externally present inertia, so that the anti-interference performance of the system is improved. No actual excitation regulator or excitation power unit is present in the virtual excitation system, which regulates the reactive power and the voltage only by synchronizing the externality of the generator excitation system.
In a virtual governor, the angular expression of the VSG is:
wherein P is ref For reference rated power, P e For electromagnetic power omega n For the nominal angular frequency, J is the moment of inertia and D is the damping coefficient.
The virtual excitation system of the VSG is expressed as:
e is the amplitude of the output voltage of the inverter bridge arm of the VSG; k (K) q Is the voltage regulation coefficient of VSG, which reflects the deficiencyThe ability of the excitation controller to regulate voltage; q (Q) ref A set point representing a reference reactive power of the VSG; q represents the reactive power actually output by the VSG; d (D) q The droop coefficient representing the adjustment coefficient of reactive power, i.e. reactive power and voltage; u represents the actual voltage amplitude of the VSG output; u (U) ref Representing the magnitude of the reference voltage output by the VSG.
S103, determining an initial value of the moment of inertia and an initial value of a damping coefficient of the virtual speed regulator through a machine learning algorithm.
The embodiment adopts a reinforcement learning Q-learning algorithm to determine the initial value J of the moment of inertia in the VSG 0 And damping coefficient initial value D 0 The method specifically comprises the following steps:
s201, inputting iteration times T, an action space A, a state space S, an exploration rate E, an attenuation coefficient gamma and an updating step length alpha, and outputting an action cost function Q (S, a), wherein S is a state, and a is an action;
s202, randomly initializing an action cost function Q (S, a), and setting the Q value of a termination state to be 0;
s203, initializing S as the first state of the current sequence;
s204, selecting a corresponding action a from the state S by adopting an E-greedy method;
s205, the agent interacts with the environment, and executes the selected action a to obtain a new state S' and a corresponding reward R;
s206, updating the action cost function Q (S, a) by using a time sequence difference method:
Q(s,a)＝Q(s,a)+[R t +γ*max α Q(s′,α)-Q(s,a)]；
s207, updating the state, wherein s=s';
s208, if S' meets the stop condition, completing the iteration of the current round, otherwise, turning to step S204;
s209, when the network training is completed, namely the action cost function Q (S, a) is converged, corresponding action a is selected in different initial value states S for different VSG systems, so that the initial value of the moment of inertia and the initial value of the damping coefficient of the VSG systems are determined. The initial value of the moment of inertia and the initial value of the damping coefficient are taken as the precondition and the basis for setting the self-adaptive rate, and the setting quality of the initial value of the moment of inertia and the initial value of the damping coefficient can directly influence the dynamic performance of the system.
The core in the algorithm described above is the design and selection of state sets, action sets and bonus functions. In the VSG control, the overshoot σ of the state set selection system, the action set is set to adjust the values of the moment of inertia J and the damping coefficient D, and the bonus function is set as shown in the following formula:
wherein R is t As a reward function, sigma% is overshoot, t r Is the rise time.
S104, designing an adaptive rate based on the initial value of the rotational inertia and the initial value of the damping coefficient of the virtual speed regulator, and generating a VSG control strategy based on fractional order adaptation.
In VSG systems, the moment of inertia and damping coefficient have some effect on the dynamic performance of the system. When the damping coefficient is fixed, the larger the moment of inertia is, the smaller the overshoot of the system is, but when the moment of inertia is too large, the system loses stability; and when the moment of inertia is fixed, the larger the set damping coefficient is, the smaller the overshoot of the system is. In the VSG control strategy based on fractional order self-adaption, the moment of inertia is adjusted through the change rate of the angular frequency, and the damping coefficient is adjusted through the change amount of the angular frequency, so that the active oscillation of the system is restrained.
In this embodiment, the adaptive rate is designed to generate a VSG control strategy based on fractional order adaptation, and the method specifically includes the following steps:
s301, analyzing a rotor angular frequency oscillation curve graph to divide an oscillation period into t 1 To t 2 、t 2 To t 3 、t 3 To t 4 、t 4 To t 5 Four time periods. Referring to fig. 3, the angular frequency increment and the rate of change of the angular frequency are different at each time period. Through analysis of the oscillation process, functional relationships between the moment of inertia and the angular frequency change rate, and between the damping coefficient and the angular frequency increment can be respectively constructed.
S302, analyzing the oscillation process in four time periods, and respectively constructing functional relations between the rotation inertia and the angular frequency change rate and between the damping coefficient and the angular frequency increment in the corresponding time periods.
At t 1 At this time, as the system load increases, the active power outside the system increases, causing oscillation of the angular frequency.
At t 1 -t 2 In the time period, the deviation delta omega of the angular velocity of the system is larger than 0, and the change rate dω/dt of the angular velocity is also larger than 0, which corresponds to the forward acceleration motion in which the acceleration is reduced. At this time, the moment of inertia should be increased to reduce the overshoot, so as to prevent the angular frequency offset from being too large, and make the system unstable. At the same time, in order to reduce the rate of change of angular frequency deviation, the damping coefficient should be reduced so that the system reaches the peak faster.
At t 2 -t 3 In the time period, the deviation amount delta omega of the angular velocity of the system is larger than 0, the change rate domega/dt of the angular velocity is smaller than 0, and the time period corresponds to the forward deceleration motion of increasing acceleration. In order to suppress the oscillation and stabilize the damping of the angular frequency as soon as possible, the damping coefficient should be appropriately increased at this time. At the same time, if the system is given a small moment of inertia during this forward deceleration phase, this will bring the rotor back to a steady value with a large acceleration.
At t 3 -t 4 In the time period, the deviation delta omega of the angular velocity of the system is smaller than 0, the change rate domega/dt of the angular velocity is smaller than 0, and the reverse acceleration motion which is performed with acceleration reduction is equivalent. At this time and t 1 -t 2 In a similar period of time, the moment of inertia should be increased to prevent the angular frequency offset from being too large, and the system from losing stability. Meanwhile, in order to reduce the rate of change of the degree deviation, the damping coefficient should be reduced.
At t 4 -t 5 In the time period, the deviation delta omega of the angular velocity of the system is smaller than 0, and the change rate dω/dt of the angular velocity is larger than 0, which corresponds to the reverse deceleration motion in which the acceleration is increased. At this time and t 2 -t 3 The moment of inertia should be reduced and the damping coefficient increased similarly.
From the above analysis, the adaptive law to be followed by the moment of inertia J and the damping coefficient D can be obtained as shown in table 1:
table 1 setting of adaptation rate
S303, respectively constructing a damping coefficient D and a self-adaptive formula of the moment of inertia J based on fractional order according to the function relation constructed in the previous step.
Referring to fig. 4 and 5, the adaptive formula of the damping coefficient D is as follows:
the adaptive formula for moment of inertia J is as follows:
wherein J is 0 And D 0 The moment of inertia and the damping coefficient of the system in steady state are respectively; k (K) J And K D Adaptive coefficients representing the moment of inertia and damping coefficient, respectively; n (N) 1 For adjusting threshold value of angular frequency variation, N 2 Is a threshold value for the amount of angular frequency deviation.
In this embodiment, the moment of inertia is first adjusted by the rate of change of the angular frequency variation, and the damping coefficient is adjusted by the angular frequency variation. N (N) 1 And N 2 The threshold is used for judging whether to perform self-adaptive adjustment of the moment of inertia and the damping coefficient, if the threshold is set to be too small, the normal operation of the VSG is not facilitated, but the setting to be too large influences the precision of the self-adaptive adjustment. When the change rate of the angular frequency is smaller, the self-adaptive adjustment of the moment of inertia is not performed; while at angular frequencyWhen the variation is smaller, the self-adaptive adjustment of the damping coefficient is not performed.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (7)
1. A fractional order self-adaption-based micro-grid VSG control method, which is characterized by comprising the following steps:
s101, establishing a circuit topology structure of a voltage sag generator (Voltage Sag Generator, VSG);
s102, modeling a virtual speed regulator and a virtual excitation system of the VSG;
s103, determining an initial value of the moment of inertia and an initial value of a damping coefficient of the virtual speed regulator through a machine learning algorithm;
s104, designing an adaptive rate based on the initial value of the rotational inertia and the initial value of the damping coefficient of the virtual speed regulator, and generating a VSG control strategy based on fractional order adaptation.
2. The fractional order self-adaption-based micro-grid VSG control method of claim 1 is characterized by modeling a virtual speed regulator and a virtual excitation system of the VSG, and specifically comprises the following steps:
in a virtual governor, the angular expression of the VSG is:
wherein P is ref For reference rated power, P e For electromagnetic power omega n For rated angular frequency, J is rotational inertia, D is damping coefficient;
the virtual excitation system of the VSG is expressed as:
e is the amplitude of the output voltage of the inverter bridge arm of the VSG; k (K) q The voltage regulation coefficient of VSG is the voltage regulation coefficient reflecting the voltage regulation capability of the virtual excitation controller; q (Q) ref A set point representing a reference reactive power of the VSG; q represents the reactive power actually output by the VSG; d (D) q The droop coefficient representing the adjustment coefficient of reactive power, i.e. reactive power and voltage; u represents the actual voltage amplitude of the VSG output; u (U) ref Representing the magnitude of the reference voltage output by the VSG.
3. The fractional order self-adaptive micro-grid VSG control method according to claim 1, wherein in step S3, the initial value J of the moment of inertia in the VSG is determined by a reinforcement learning Q-learning algorithm 0 And damping coefficient initial value D 0 。
4. The fractional order self-adaptive micro-grid VSG control method according to claim 3, wherein the initial moment of inertia J in the VSG is determined by a reinforcement learning Q-learning algorithm 0 And damping coefficient initial value D 0 The method specifically comprises the following steps:
s201, inputting iteration times T, an action space A, a state space S, an exploration rate E, an attenuation coefficient gamma and an updating step length alpha, and outputting an action cost function Q (S, a), wherein S is a state, and a is an action;
s202, randomly initializing an action cost function Q (S, a), and setting the Q value of a termination state to be 0;
s203, initializing S as the first state of the current sequence;
s204, selecting a corresponding action a from the state S by adopting an E-greedy method;
s205, the agent interacts with the environment, and executes the selected action a to obtain a new state S' and a corresponding reward R;
s206, updating the action cost function Q (S, a) by using a time sequence difference method:
Q(s,a)＝Q(s,a)+[R t +Y*maX a Q(s′,a)-Q(s,a)]:
s207, updating the state, wherein s=s';
s208, if S' meets the stop condition, completing the iteration of the current round, otherwise, turning to step S204;
s209, when the action cost function Q (S, a) converges, selecting corresponding action a in different initial value states S for different VSG systems, so as to determine the initial value of the moment of inertia and the initial value of the damping coefficient of the VSG system.
5. The fractional order self-adaptive micro-grid VSG control method according to claim 4, wherein the initial moment of inertia J is determined in VSG by reinforcement learning Q-learning algorithm 0 And damping coefficient initial value D 0 When the state set of the reinforcement learning Q-learning algorithm is set as the overshoot sigma of the system, the action set is set as the values of the adjustment moment of inertia J and the damping coefficient D, and the reward function is set as shown in the following formula:
wherein R is t As a reward function, sigma% is overshoot, t r Is the rise time.
6. The micro grid VSG control method based on fractional order adaptation according to claim 1, wherein the adaptive rate is designed to generate the VSG control strategy based on fractional order adaptation, and specifically comprising the following steps:
s301, analyzing a rotor angular frequency oscillation curve graph to divide an oscillation period into t 1 To t 2 、t 2 To t 3 、t 3 To t 4 、t 4 To t 5 Four time periods;
s302, analyzing the oscillation process in four time periods, and respectively constructing functional relations between rotation inertia and angular frequency change rate, damping coefficient and angular frequency increment in corresponding time periods;
s303, respectively constructing a damping coefficient D and a self-adaptive formula of the moment of inertia J based on fractional order according to the function relation constructed in the previous step.
7. The fractional order self-adaption-based microgrid VSG control method according to claim 6 is characterized in that the self-adaption formula of the damping coefficient D is as follows:
the adaptive formula for moment of inertia J is as follows:
wherein J is 0 And D 0 The moment of inertia and the damping coefficient of the system in steady state are respectively; k (K) J And K D Adaptive coefficients representing the moment of inertia and damping coefficient, respectively; n (N) 1 For adjusting threshold value of angular frequency variation, N 2 Is a threshold value for the amount of angular frequency deviation.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021012298A1 (en) * | 2019-07-25 | 2021-01-28 | 东北大学 | Self-mutual-group multi-level stability identification and stability recovery method for multi-port energy router |
| CN116247750A (en) * | 2023-03-30 | 2023-06-09 | 中国矿业大学 | Inertia and damping self-adaptive VSG control method |
-
2023
- 2023-07-13 CN CN202310865082.5A patent/CN116914829A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021012298A1 (en) * | 2019-07-25 | 2021-01-28 | 东北大学 | Self-mutual-group multi-level stability identification and stability recovery method for multi-port energy router |
| CN116247750A (en) * | 2023-03-30 | 2023-06-09 | 中国矿业大学 | Inertia and damping self-adaptive VSG control method |
Non-Patent Citations (4)
| Title |
|---|
| 李万清 编: "《高等学校人工智能教育丛书 MindSpore深度学习入门与实践》", 31 August 2022, 西安电子科学技术大学出版社, pages: 67 * |
| 滕奇: ""基于虚拟同步机的逆变器协调控制策略研究"", 中国优秀硕士学位论文全文数据库（电子期刊）, no. 2023, 15 February 2023 (2023-02-15), pages 10 * |
| 王洲 等: ""基于 Q 学习的光伏储能微网 VSG 与APF协调优化研究"", 可再生能源, vol. 39, no. 7, 31 July 2021 (2021-07-31), pages 983 * |
| 程静: ""基于虚拟同步机的风光储并网***自适应功频控制策略"", 现代电力, vol. 40, 26 June 2023 (2023-06-26), pages 1 - 8 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117691648A (en) * | 2024-02-04 | 2024-03-12 | 国网浙江省电力有限公司经济技术研究院 | Control method, system, equipment and storage medium of network-structured energy storage converter |
| CN117691648B (en) * | 2024-02-04 | 2024-04-26 | 国网浙江省电力有限公司经济技术研究院 | Control method, system, equipment and storage medium of network-structured energy storage converter |
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