Distributed control method and system for renewable energy converter

Abstract

The invention discloses a renewable energy converter-oriented distributed control method and system. The method comprises the following steps of S1, establishing an alternating current micro-grid system comprising a plurality of renewable energy converters, a sparse communication network and a multi-agent system; s2, calculating to obtain frequency and active power; s3, constructing a distributed event triggering secondary control strategy; s4, modulating the voltage and the frequency into a three-phase PWM switch control signal, and applying the three-phase PWM switch control signal to the renewable energy converter; according to the renewable energy converter distributed control method disclosed by the invention, the frequency deviation caused by virtual synchronous machine control is eliminated, so that the active power distribution achieves an expected effect; according to the disclosed event triggering secondary control method, calculation and communication burdens are effectively reduced, and stable operation of the system in a load fluctuation scene is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of renewable energy converter control technology, specifically a distributed control method and system for renewable energy converters. Background Technology

[0002] In recent years, the development and utilization of renewable energy has received widespread attention. Most renewable energy generates electricity in the form of direct current (DC), which must be converted into electricity by inverters, thus usually forming a microgrid system with inverters at its core. Unlike traditional synchronous generators, the output voltage of power electronic devices is completely controlled and has an extremely rapid dynamic response. When converters are widely used in power systems, the overall inertia level of the microgrid system is low, and the system will exhibit a lack of inertia, making it more prone to frequency oscillations when grid faults occur. This problem is even more serious for microgrids with a high proportion of renewable energy and power electronic devices.

[0003] To enhance the inertia of microgrid systems, Virtual Synchronous Generator (VSG) control has attracted significant attention in inverter control research. VSG control is a grid-based control mechanism that can be used to control grid-based converters, enabling them to participate in maintaining the microgrid's frequency and voltage. When adjusting the converter's output frequency and voltage, VSG control simulates the operating characteristics of a synchronous generator, imbuing it with inertial features and injecting simulated inertia into the system. This design effectively enhances the dynamic stability of the microgrid. Furthermore, VSG also mimics the power-sharing capability of a synchronous generator by performing droop control of frequency and voltage, thereby enabling power distribution among distributed power sources.

[0004] However, the VSG control function still has some shortcomings:

[0005] Firstly, virtual synchronous machine control technology is widely used in renewable energy converters, which achieves stable power output by simulating the external characteristics of synchronous generators. However, this control technology can cause frequency deviation due to its droop characteristics, and existing secondary control methods for this problem often require modification of the internal structure or parameters of the virtual synchronous machine controller, making it difficult to apply directly to existing systems and thus limiting the improvement of control performance.

[0006] Secondly, with the widespread application of renewable energy converters in AC microgrids, the demand for multi-device collaborative control is becoming increasingly prominent. Traditional centralized control relies on a central controller, which suffers from high communication bandwidth pressure and poor fault tolerance. While distributed control based on multi-agent theory has the advantage of local interaction, in the scenario of renewable energy converters, its precise frequency control and reasonable allocation of active power have not yet achieved ideal results, and it cannot effectively eliminate the frequency deviation caused by virtual synchronous machine control.

[0007] Third, in distributed control, continuous real-time communication is usually used between agents to ensure control accuracy, which brings a large computational and communication burden. This is especially true in AC microgrid environments with frequent load fluctuations, where the drawbacks of continuous communication are more obvious. Existing distributed control methods lack effective optimization mechanisms for communication and computational burdens, making it difficult to achieve a good balance between control performance and system resource consumption.

[0008] Therefore, it is necessary to design a distributed control method and system for renewable energy converters. Summary of the Invention

[0009] The purpose of this invention is to provide a distributed control method and system for renewable energy converters, in order to solve the problems mentioned in the background art, such as frequency deviation caused by the drooping characteristics of virtual synchronous machine control, poor adaptability of existing secondary control methods, poor frequency accuracy control and active power allocation under the collaborative control of multiple renewable energy converters, and excessive computing and communication burden caused by continuous real-time communication in distributed control.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] Firstly, a distributed control method for renewable energy converters is provided, comprising the following steps:

[0012] S1: Establish an AC microgrid system consisting of multiple renewable energy converters. Each renewable energy converter measures its own three-phase output voltage and output current, calculates its own frequency and active power, and communicates with adjacent renewable energy converters through a communication system to obtain information.

[0013] S2: Based on the virtual synchronous machine control, a virtual synchronous machine power reference value adjustment method based on multi-agent theory and event triggering mechanism is added to construct a distributed event-triggered secondary control strategy, and the stability of the control strategy is proven.

[0014] Based on the correlation characteristics of frequency, phase, and power, a single renewable energy converter can achieve a steady state synchronized with the global frequency of the microgrid, satisfying the following relationship:

[0015]

[0016] in, For VSG frequency, For the rated frequency, For the first The active power output of the renewable energy converter in Taiwan. For the first The active power rating of the renewable energy converter in Taiwan. For the first Active frequency droop factor of Taiwan renewable energy converter This represents the global steady-state frequency of the microgrid.

[0017] The differential control characteristics of virtual synchronous machines also hold true at the system level, and microgrids composed of multiple converters satisfy the following:

[0018]

[0019] in, This represents the total power of the microgrid.

[0020] Furthermore, the relationship between the global steady-state frequency and total power of the microgrid is derived:

[0021]

[0022] The distributed control strategy of this invention transmits corrective consistency variables among agents. This enables all agents to achieve global consistency. And adjust based on this global information To achieve frequency recovery and power distribution, satisfying:

[0023]

[0024] in, For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, For the first Neighborhood set of Taiwan renewable energy converters This represents the number of iterations.

[0025] Will Total power of microgrid By matching and combining the active-frequency equation, the global frequency can be restored to its rated value:

[0026]

[0027] For a single converter, the following conditions are met:

[0028]

[0029] The event triggering mechanism is based on conditions. Based on, To trigger the parameters, the real-time information update is changed to intermittent update, at which point the system steady-state frequency fluctuates around the rated value:

[0030]

[0031] To prove the stability of the control strategy, the Lyapunov energy function is constructed:

[0032]

[0033] in, , For total power matching, a globally consistent variable is defined, satisfying the condition that... , , and when , ;

[0034] By combining the derivation, we can obtain Since the derivative is not positive, the system is stable.

[0035] S3: The voltage and frequency obtained from the distributed event-triggered secondary control strategy are transformed into a three-phase AC voltage waveform through dq transformation, and then further modulated into a three-phase PWM switching control signal, which is applied to the renewable energy converter.

[0036] As a further technical solution of the present invention, in S1, the basic components of the AC microgrid system are renewable energy converters, filter circuits, line impedances, and loads; the sparse communication network is a communication network in the AC microgrid system where the number of communication connections between different renewable energy converters is less than the number of electrical connections and ensures connectivity; the information is a consistency variable of the multi-agent system; in the multi-agent system, each renewable energy converter is regarded as a node, and two nodes are neighboring nodes when there is a communication connection between them.

[0037] As a further technical solution of the present invention, in S2, the distributed event-triggered secondary control strategy includes two core parts: multi-agent distributed control and event-triggered control; the multi-agent distributed control adjusts the rated power according to the information to achieve frequency recovery and power allocation; the event-triggered control makes the communication intermittent.

[0038] As a further technical solution of the present invention, in S2, the expression for multi-agent distributed control is as described above:

[0039]

[0040] in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, This is the reference value for the active power of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. For rotational inertia, The damping coefficient is... Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. Let i be the consistency variable corresponding to the i-th renewable energy converter. For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, Let i be the neighborhood set of the i-th renewable energy converter. This represents the number of iterations.

[0041] As a further technical solution of the present invention, in S2, the triggering conditions for event triggering control are as described above:

[0042]

[0043] in, These are parameters for the event triggering conditions. Let i be the active power output of the i-th renewable energy converter. Let be the active power rating of the i-th renewable energy converter.

[0044] As a further technical solution of the present invention, in step S2, after adding the event triggering control, the expression of the distributed event triggering secondary control strategy is:

[0045]

[0046] in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, This is the reference value for the active power of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. For rotational inertia, The damping coefficient is... Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. Let i be the consistency variable corresponding to the i-th renewable energy converter. For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, Let i be the neighborhood set of the i-th renewable energy converter. For the number of iterations, This is a parameter for the event triggering condition.

[0047] As a further technical solution of the present invention, in step S2, after adding the event trigger control, the steady-state frequency of the system satisfies the relationship described above:

[0048]

[0049] in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. This is a parameter for the event triggering condition.

[0050] As a further technical solution of the present invention, in S2, when the distributed event-triggered secondary control strategy is used for stability proof, the constructed Lyapunov energy function is:

[0051]

[0052] The Lyapunov energy function satisfies the following condition:

[0053]

[0054] in, , Let i be the consistency variable corresponding to the i-th renewable energy converter. For the number of renewable energy converters, The constant is the value when the time is equal to the time ... The rated total power is matched with the total load power. It serves as a global consistency variable for multi-agent systems.

[0055] As a further technical solution of the present invention, in step S3, the expression for the communication weight matrix in the multi-agent system is:

[0056]

[0057] in, For communication weights among multiple agents, Let i be the number of individuals in the neighborhood of the i-th renewable energy converter. Let j be the number of individuals in the neighborhood of the j-th renewable energy converter. To ensure the stability of the system, positive numbers, Let i be the neighborhood set of the i-th renewable energy converter;

[0058] The consistency variable With active power rating The relationship is as described above:

[0059]

[0060] in, These are the power allocation parameters.

[0061] Secondly, a distributed control system for renewable energy converters is provided, including:

[0062] An AC microgrid module includes multiple renewable energy converters, filter circuits, line impedance, and loads;

[0063] A sparse communication network module is used to realize information interaction between different renewable energy converters in an AC microgrid module, wherein the information is a consistency variable of a multi-agent system.

[0064] A multi-agent construction module is established based on the AC microgrid module to build a multi-agent system. In the multi-agent system, each renewable energy converter is regarded as a node, and two nodes are neighbor nodes when there is a communication connection between them.

[0065] The parameter measurement and communication module enables each renewable energy converter to measure its own three-phase output voltage and output current, calculate its own frequency and active power, and communicate with neighboring agents in the sparse communication network module to obtain information.

[0066] The control strategy construction module is used to add a virtual synchronous machine power reference value adjustment method based on multi-agent theory and event triggering mechanism to the virtual synchronous machine control, construct a distributed event-triggered secondary control strategy, and prove the stability of the control strategy.

[0067] The signal processing and application module is used to convert the voltage and frequency obtained from the distributed event-triggered secondary control strategy into a three-phase AC voltage waveform through dq transformation, and further modulate it into a three-phase PWM switching control signal, which is then applied to the renewable energy converter.

[0068] Compared with existing technologies, the advantages of this distributed control method and system for renewable energy converters are:

[0069] For renewable energy converters using virtual synchronous machine control, an improved secondary control strategy is proposed. This strategy does not involve the internal structure and parameters of the virtual synchronous machine controller and can be directly applied to existing AC microgrid systems containing multiple renewable energy converters. It can enhance control performance, effectively eliminate frequency deviation caused by the droop characteristics of the virtual synchronous machine, stabilize the system frequency near the rated frequency, and improve the active power distribution accuracy.

[0070] Based on multi-agent theory, a distributed control method for renewable energy converters is proposed. Through local interaction and coordination among nodes in the multi-agent system, the active power rating of each renewable energy converter is adjusted. The frequency of the renewable energy converter is precisely controlled at the rated value, eliminating the frequency deviation caused by the virtual synchronous machine control, so that the active power distribution achieves the expected effect.

[0071] Combining event-triggered mechanisms, a secondary control method based on event-triggered mechanisms is proposed on the basis of distributed control; based on triggering conditions... The timing of system state updates is controlled, and the system state is updated or adjusted only when specific events occur, effectively reducing the computational and communication burden and ensuring stable system operation under load fluctuation scenarios. Attached Figure Description

[0072] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0073] Figure 2 This is the overall control block diagram of the present invention;

[0074] Figure 3 This is a circuit diagram of a single busbar topology in an embodiment of the present invention. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please see the appendix Figure 1 - Appendix Figure 2 The present invention provides an embodiment 1: a distributed control method for renewable energy converters, comprising the following steps:

[0077] S1: Establish an AC microgrid system containing multiple renewable energy converters. Each renewable energy converter measures its own three-phase output voltage and output current, calculates its own frequency and active power, and communicates with neighboring intelligent agents in a sparse communication network to obtain information.

[0078] The basic components of an AC microgrid system are renewable energy converters, filter circuits, line impedance, and loads; a sparse communication network is a communication network in an AC microgrid system where the number of communication connections between different renewable energy converters is less than the number of electrical connections, while ensuring connectivity; information is a consistency variable in a multi-agent system; in a multi-agent system, each renewable energy converter is treated as a node, and two nodes are considered neighbors when there is a communication connection between them.

[0079] S2: Based on the virtual synchronous machine control, a power reference value adjustment method for the virtual synchronous machine based on multi-agent theory and event triggering mechanism is added to construct a distributed event-triggered secondary control strategy, and the stability of this control strategy is proven. The distributed event-triggered secondary control strategy includes two core parts: multi-agent distributed control and event-triggered control. Multi-agent distributed control adjusts the rated power according to information to achieve frequency recovery and power distribution. Event-triggered control makes the communication intermittent.

[0080] The expression for multi-agent distributed control is:

[0081]

[0082] in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, This is the reference value for the active power of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. For rotational inertia, The damping coefficient is... Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. Let i be the consistency variable corresponding to the i-th renewable energy converter. For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, Let i be the neighborhood set of the i-th renewable energy converter. This represents the number of iterations.

[0083] The triggering conditions for event-triggered control are:

[0084]

[0085] in, These are parameters for the event triggering conditions. Let i be the active power output of the i-th renewable energy converter. Let be the active power rating of the i-th renewable energy converter;

[0086] After adding event-triggered control, the expression for the distributed event-triggered secondary control strategy is:

[0087]

[0088] in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, This is the reference value for the active power of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. For rotational inertia, The damping coefficient is... Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. Let i be the consistency variable corresponding to the i-th renewable energy converter. For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, Let i be the neighborhood set of the i-th renewable energy converter. For the number of iterations, These are parameters for the event triggering conditions;

[0089] After adding event-triggered control, the steady-state frequency of the system satisfies the following relationship:

[0090]

[0091] in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. These are parameters for the event triggering conditions;

[0092] When proving the stability of a distributed event-triggered quadratic control strategy, the constructed Lyapunov energy function is:

[0093]

[0094] The Lyapunov energy function satisfies the following condition:

[0095]

[0096] in, , Let i be the consistency variable corresponding to the i-th renewable energy converter. For the number of renewable energy converters, The constant is the value when the time is equal to the time ... The rated total power is matched with the total load power. For global consistency variables in multi-agent systems;

[0097] In a multi-agent system, the expression for the communication weight matrix is:

[0098]

[0099] in, For communication weights among multiple agents, Let i be the number of individuals in the neighborhood of the i-th renewable energy converter. Let j be the number of individuals in the neighborhood of the j-th renewable energy converter. To ensure the stability of the system, positive numbers, Let i be the neighborhood set of the i-th renewable energy converter;

[0100] Consistent variables With active power rating satisfy:

[0101]

[0102] in, Power allocation parameters;

[0103] S3: The voltage and frequency obtained from the distributed event-triggered secondary control strategy are transformed into a three-phase AC voltage waveform through dq transformation, and then further modulated into a three-phase PWM switching control signal, which is applied to the renewable energy converter.

[0104] An embodiment 2 of the present invention provides: a distributed control system for renewable energy converters, comprising:

[0105] An AC microgrid module includes multiple renewable energy converters, filter circuits, line impedance, and loads;

[0106] The sparse communication network module is used to realize information interaction between different renewable energy converters in the AC microgrid module, and the information is a consistency variable of the multi-agent system;

[0107] The multi-agent construction module establishes a multi-agent system based on the AC microgrid module. In the multi-agent system, each renewable energy converter is treated as a node, and two nodes are neighboring nodes when there is a communication connection between them.

[0108] The parameter measurement and communication module enables each renewable energy converter to measure its own three-phase output voltage and output current, calculate its own frequency and active power, and communicate with neighboring agents in the sparse communication network module to obtain information.

[0109] The control strategy construction module is used to add a virtual synchronous machine power reference value adjustment method based on multi-agent theory and event triggering mechanism to the virtual synchronous machine control, construct a distributed event-triggered secondary control strategy, and prove the stability of the control strategy.

[0110] The signal processing and application module is used to convert the voltage and frequency obtained from the distributed event-triggered secondary control strategy into a three-phase AC voltage waveform through dq transformation, and further modulate it into a three-phase PWM switching control signal, which is then applied to the renewable energy converter.

[0111] Please see the appendix Figure 3 The present invention provides an embodiment 3: a distributed control method for renewable energy converters, comprising the following steps:

[0112] S1: Establish an AC microgrid system containing multiple renewable energy converters. Each renewable energy converter measures its own three-phase output voltage and output current, calculates its own frequency and active power, and communicates with neighboring agents in a sparse communication network to obtain information. This embodiment analyzes a single bus topology, including four renewable energy converters.

[0113] S2: Based on the virtual synchronous machine control, a virtual synchronous machine power reference value adjustment method based on multi-agent theory and event triggering mechanism is added to construct a distributed event-triggered secondary control strategy, and the stability of the control strategy is proven.

[0114] Derivation process:

[0115] Virtual synchronous machine (VSM) control, as a control strategy for renewable energy converters, aims to enable the converter to simulate the operating characteristics of a synchronous generator. It actively adjusts the frequency and amplitude of its output voltage to establish and support stable voltage and frequency references for islanded AC microgrids, while ensuring that each grid-connected converter can autonomously allocate power according to its own capacity. For renewable energy converters using VSM control, the frequency is determined by the output power and the VSM control parameters, as follows:

[0116]

[0117] in, For VSG frequency, For the rated frequency, For rotational inertia, The damping coefficient is... This is a reference value for active power. The active power output of the renewable energy converter. This is the rated active power. The droop factor is the active frequency. For time;

[0118] Under virtual synchronous machine control, the steady-state active power-frequency control result of the converter exhibits a droop characteristic, specifically as follows:

[0119]

[0120] For a microgrid system composed of multiple renewable energy converters with droop characteristics, the active power of the converters is directly related to the phase difference; for high-voltage systems, the line impedance is considered to be mainly inductive, and the active power output of the microgrid can be approximated as:

[0121]

[0122] in, This is the effective value of the power supply output voltage. This is the effective value of the AC bus voltage. This is the impedance between the power source and the bus. The phase of the power supply voltage relative to the AC bus voltage;

[0123] When a converter's frequency is higher than the system frequency, its output voltage phase is relatively ahead, thus outputting more active power. According to the droop characteristic, converters with higher output active power exhibit a lower frequency, while those with lower output power exhibit the opposite. Due to the relationship between frequency, phase, and power, this adjustment will inevitably achieve a steady state synchronized with the overall microgrid frequency, resulting in the following relationship:

[0124]

[0125] in, This refers to the global frequency of the microgrid in steady state.

[0126] Virtual synchronous machine control is a communication-free differential control system, characterized by its inherent deviation. When the actual transmitted power differs from the set reference power, the converter's frequency will deviate from its rated value. This characteristic also applies at the system level; a microgrid consisting of multiple converters controlled by virtual synchronous machines will exhibit an overall steady-state frequency that deviates from its rated value as the total load power changes. In other words, a microgrid system composed of multiple converters will display the same overall characteristics, specifically as follows:

[0127]

[0128] in, This represents the total power of the microgrid.

[0129] It can be equivalently considered that a microgrid droop coefficient exists. and microgrid power rating respectively satisfy and Substituting the values, the relationship between the global steady-state frequency and total power of the microgrid is obtained as follows:

[0130]

[0131] This formula represents active power-frequency control at the system level. It can be further observed that when the total power of the microgrid does not match the total rated power set by each renewable energy converter, the system frequency will deviate from the rated value.

[0132] For isolated microgrids, there exists a rule that total power equals total load, specifically as follows:

[0133]

[0134] in, The total load of the microgrid;

[0135] The total load of a microgrid varies with load changes and may not always perfectly match the set virtual synchronous machine power reference value, resulting in the system frequency deviating from the rated value. This is an inevitable result of primary control. To solve this problem, measures are taken in secondary control to eliminate frequency deviation. Common secondary control methods include centralized control, master-slave control, and distributed control. Among them, distributed control based on multi-agent systems has received special attention due to its lower implementation difficulty, higher flexibility, and stronger fault tolerance.

[0136] Multi-agent consensus theory aims to solve the problem of how multiple autonomous agents in a distributed system can achieve consistency in state or action through local interaction and cooperation. Its core is to design distributed algorithms and protocols so that each agent can achieve consistent behavior or state of the whole system through information interaction with neighboring nodes and local policy updates.

[0137] For those with In a multi-agent system with multiple nodes, if a communication link exists... Connecting renewable energy converters and renewable energy converters Then renewable energy converter Considered as a neighborhood set In, it means In a multi-agent system, a single renewable energy converter Only from the neighborhood set Information is received; simultaneously, considering the differences between agents in the communication topology, the impact of information from each neighbor on the local control strategy is usually adjusted based on the communication weight matrix; a common form of the communication weight matrix is ​​as follows:

[0138]

[0139] in, For communication weights among multiple agents, For renewable energy converters The number of individuals in the neighborhood, A positive integer representing the system stability;

[0140] Based on the communication weight matrix, the following multi-agent control method can be formed:

[0141]

[0142] in, For consistency variables;

[0143] This control strategy allows Information is transmitted and corrected among individual agents, ultimately ensuring that each agent in a multi-agent system receives globally consistent information. ; Utilize global information right Adjustments are made to achieve the control objectives of frequency recovery and power distribution;

[0144] To achieve this goal, two measures were taken:

[0145] First, the regulations With each converter The following relationship must be satisfied:

[0146]

[0147] in, These are parameters used for power distribution;

[0148] At this time, synchronized and The following linear relationship will be satisfied:

[0149]

[0150] Secondly, during the control process, based on the actual output power With VSG rated power Difference adjustment The value, implementation and Matching:

[0151]

[0152] in, This is the deviation correction factor;

[0153] Through these two measures, Corresponding adjustments Total power of microgrid match:

[0154]

[0155] Combined with the active-frequency equation, that is:

[0156]

[0157] Therefore, the frequency of the microgrid system is unbiased, and the frequency of any converter within the system is also unbiased.

[0158]

[0159] While controlling the frequency to its rated value, the power of any converter is equal to its power rating, which is subject to a consistency variable. And the effect of setting parameters:

[0160]

[0161] If the goal is to distribute power evenly among the converters, then set... This ensures that the active power output of each converter is consistent; secondary control requires the execution of power commands from tertiary control, and settings are required. ,in The power command obtained from the three-level control strategy is generally guaranteed to match the total system power command with the total power. Therefore, when applying three-level control, the power command obtained is... The result; however, the time interval of the three-level control is relatively long, during which the total power and load may change; at this time, the role of the secondary control is to correct this command so that the frequency does not deviate from the rated value, and the correction command is evenly distributed on each power supply;

[0162] Multi-agent systems require converters to communicate simply with neighboring agents. To effectively reduce the computational and communication burden on the controller, event-triggered control is added. This control is a communication control strategy that updates or adjusts the system's state or output only when a specific event occurs. It is typically used in distributed control systems to improve efficiency and responsiveness.

[0163] The control strategy proposed in this invention is The iteration process involves two parts, one of which is through the communication network. The consensus was reached, and another part involved correction based on the deviation between the power rating and the actual power output. The iterative process utilized information from adjacent converters, thus requiring a reliable communication network. To reduce communication load, an event-triggered mechanism was introduced. This mechanism, based on preset trigger conditions, transformed continuous real-time information updates into intermittent updates that only occurred when the conditions were met. The event trigger conditions are as follows:

[0164]

[0165] in, These are parameters for the event triggering conditions;

[0166] The trigger condition is determined every 0.1 seconds; when the converter... When the conditions are met, it will release its immediate... Information is provided to neighbors to update their local data; otherwise, each neighbor node retains and uses a copy of the last received data.

[0167] While effectively reducing communication load, event-triggered mechanisms also introduce a trade-off: they may prevent communication-dependent control objectives from being achieved with complete precision. Specifically, the system's steady-state frequency may not be strictly maintained at its rated value, but rather fluctuates within an acceptable range around it.

[0168]

[0169] Such steady-state deviations introduced by parameter constraints are within the design tolerance range, and such parameter constraint deviations are acceptable.

[0170] The stability and effectiveness of the secondary control strategy proposed in this invention are demonstrated as follows:

[0171] set up And construct the Lyapunov energy function:

[0172]

[0173] in, The constant is the value when the time is equal to the time ... When the total rated power matches the total load power, it can be deduced that:

[0174]

[0175] From the iterative equation, we can obtain The derivative:

[0176]

[0177] Since the response speed of primary control is much faster than that of secondary control, and they belong to different time scales, the secondary control can be considered as acting on the steady-state result produced by the primary control during its operation. Due to frequency synchronization, for any converter:

[0178]

[0179] Depend on , , , , and The relationship between them can be obtained as follows:

[0180]

[0181] For the energy function It is obviously satisfied that:

[0182]

[0183] The energy function can be obtained. It is positive definite; it has only one zero point, that is... Simultaneously, combining the above formulas, we obtain the energy function. The derivative:

[0184]

[0185]

[0186] Therefore, the derivative of the energy function is always less than or equal to 0, only when... The value is equal to 0 only when the time is right; this proves that the system under this control strategy is stable, where... As the stable equilibrium point of the control system, the system will gradually approach this point and eventually reach a steady state; this equilibrium point also means the realization of frequency recovery and power distribution.

[0187] S3: The voltage and frequency obtained from the distributed event-triggered secondary control strategy are transformed into a three-phase AC voltage waveform through dq transformation, and then further modulated into a three-phase PWM switching control signal, which is applied to the renewable energy converter.

[0188] In summary, this invention proposes an improved secondary control strategy for renewable energy converters using virtual synchronous machine control. This strategy does not involve the internal structure and parameters of the virtual synchronous machine controller and can be directly applied to existing AC microgrid systems containing multiple renewable energy converters. It enhances control performance, effectively eliminates frequency deviations caused by the droop characteristics of the virtual synchronous machine, stabilizes the system frequency near the rated frequency, and improves the accuracy of active power distribution.

[0189] Based on multi-agent theory, a distributed control method for renewable energy converters is proposed. Through local interaction and coordination among nodes in the multi-agent system, the active power rating of each renewable energy converter is adjusted. The frequency of the renewable energy converter is precisely controlled at the rated value, eliminating the frequency deviation caused by the virtual synchronous machine control, so that the active power distribution achieves the expected effect.

[0190] Combining event-triggered mechanisms, a secondary control method based on event-triggered mechanisms is proposed on the basis of distributed control; based on triggering conditions... The timing of system state updates is controlled, and the system state is updated or adjusted only when specific events occur, effectively reducing the computational and communication burden and ensuring stable system operation under load fluctuation scenarios.

[0191] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A distributed control method for renewable energy converters, characterized in that: Includes the following steps: S1: Establish an AC microgrid system containing multiple renewable energy converters. Each renewable energy converter measures its own three-phase output voltage and output current, calculates its own frequency and power information, and communicates with adjacent renewable energy converters through a communication system to obtain information. S2: The renewable energy converter adopts virtual synchronous machine control, and on this basis, the power reference value of the renewable energy converter is adjusted based on multi-agent theory and event triggering mechanism, and the stability of the control strategy is proved; S3: The voltage and frequency obtained from the distributed event-triggered secondary control strategy are transformed into a three-phase AC voltage waveform through dq transformation, and then further modulated into a three-phase PWM switching control signal, which is applied to the renewable energy converter.

2. The distributed control method for renewable energy converters according to claim 1, characterized in that: In S1, the basic components of the AC microgrid system are renewable energy converters, filter circuits, line impedances, and loads; each renewable energy converter is considered a node, and two nodes are considered neighboring nodes when there is a communication connection between them.

3. The distributed control method for renewable energy converters according to claim 1, characterized in that: In S3, the distributed event-triggered secondary control strategy includes two core components: multi-agent distributed control and event-triggered control. The multi-agent distributed control adjusts the rated power according to the information to achieve frequency recovery and power allocation. The event-triggered control enables communication to be in an intermittent mode.

4. The distributed control method for renewable energy converters according to claim 1, characterized in that: In S3, the expression for multi-agent distributed control is: in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, This is the reference value for the active power of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. For rotational inertia, The damping coefficient is... Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. Let i be the consistency variable corresponding to the i-th renewable energy converter. For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, Let i be the neighborhood set of the i-th renewable energy converter. This represents the number of iterations.

5. The distributed control method for renewable energy converters according to claim 1, characterized in that: In S3, the triggering condition for event triggering control is: in, These are parameters for the event triggering conditions. Let i be the active power output of the i-th renewable energy converter. Let be the active power rating of the i-th renewable energy converter.

6. The distributed control method for renewable energy converters according to claim 1, characterized in that: In step S3, after adding the event triggering control, the expression for the distributed event triggering secondary control strategy is: in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, This is the reference value for the active power of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. For rotational inertia, The damping coefficient is... Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. Let i be the consistency variable corresponding to the i-th renewable energy converter. For power allocation parameters, This is the deviation correction factor. For communication weights among multiple agents, Let i be the neighborhood set of the i-th renewable energy converter. For the number of iterations, This is a parameter for the event triggering condition.

7. The distributed control method for renewable energy converters according to claim 1, characterized in that: In step S3, after the event trigger control is added, the steady-state frequency of the system satisfies the following relationship: in, Let VSG be the frequency of the i-th renewable energy converter. For the rated frequency, Let be the active frequency droop coefficient of the i-th renewable energy converter. Let i be the active power output of the i-th renewable energy converter. Let i be the active power rating of the i-th renewable energy converter. This is a parameter for the event triggering condition.

8. The distributed control method for renewable energy converters according to claim 1, characterized in that: In S3, when proving the stability of the distributed event-triggered secondary control strategy, the constructed Lyapunov energy function is: The Lyapunov energy function satisfies the following condition: in, , Let i be the consistency variable corresponding to the i-th renewable energy converter. For the number of renewable energy converters, The constant is the value when the time is equal to the time ... The rated total power is matched with the total load power. It serves as a global consistency variable for multi-agent systems.

9. A distributed control method for renewable energy converters according to claim 1, characterized in that: In S3, the expression for the communication weight matrix in the multi-agent system is: in, For communication weights among multiple agents, Let i be the number of individuals in the neighborhood of the i-th renewable energy converter. Let j be the number of individuals in the neighborhood of the j-th renewable energy converter. To ensure the stability of the system, positive numbers, Let i be the neighborhood set of the i-th renewable energy converter; The consistency variable With active power rating satisfy: in, These are the power allocation parameters.

10. A distributed control system for renewable energy converters, characterized in that: include: An AC microgrid module includes multiple renewable energy converters, filter circuits, line impedance, and loads; A sparse communication network module is used to realize information interaction between different renewable energy converters in an AC microgrid module, wherein the information is a consistency variable of a multi-agent system. A multi-agent construction module is established based on the AC microgrid module to build a multi-agent system. In the multi-agent system, each renewable energy converter is regarded as a node, and two nodes are neighbor nodes when there is a communication connection between them. The parameter measurement and communication module enables each renewable energy converter to measure its own three-phase output voltage and output current, calculate its own frequency and active power, and communicate with neighboring agents in the sparse communication network module to obtain information. The control strategy construction module is used to add a virtual synchronous machine power reference value adjustment method based on multi-agent theory and event triggering mechanism to the virtual synchronous machine control, construct a distributed event-triggered secondary control strategy, and prove the stability of the control strategy. The signal processing and application module is used to convert the voltage and frequency obtained from the distributed event-triggered secondary control strategy into a three-phase AC voltage waveform through dq transformation, and further modulate it into a three-phase PWM switching control signal, which is then applied to the renewable energy converter.

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