Microgrid finite time control method and device with event triggering mechanism

Abstract

The present disclosure relates to the technical field of power control, and provides a micro-grid finite time control method and device with an event triggering mechanism, comprising: establishing a micro-grid system model containing multiple distributed power sources; for each distributed power source, determining the frequency tracking error and state estimation error corresponding thereto based on its real-time frequency state data; and determining whether the event triggering condition is met according to the frequency tracking error and state estimation error; in response to any distributed power source meeting the event triggering condition, generating the finite time cooperative control law of each distributed power source based on the control update data of each distributed power source received when the event triggering condition is met, and generating the control instruction corresponding thereto according to the control instruction, and performing finite time frequency recovery control on the distributed power source based on the control instruction. The present embodiment effectively improves the dynamic performance and operation robustness of the power system by introducing the event triggering mechanism and designing the finite time cooperative control law.

Description

Technical Field

[0001] This disclosure relates to the field of power control technology, and more specifically, to a finite-time control method and apparatus for a microgrid with an event-triggered mechanism. Background Technology

[0002] Against the backdrop of global energy structure transformation and the integration of information technology, microgrids, as a key carrier for integrating distributed generation (DG), are becoming increasingly important. Distributed generation is widely used due to its cleanliness and flexibility, but the randomness and intermittency of its output pose a fundamental challenge to the stable operation of microgrids, causing continuous fluctuations in system frequency. Simultaneously, as a cyber-physical system, the open communication network of microgrids is vulnerable to cyberattacks such as denial-of-service (DoS) attacks, which can disrupt the transmission of control commands and further amplify system instability.

[0003] In related technologies, control methods based on periodic sampling are commonly used. However, this method struggles to adapt to both "intrinsic disturbances" and "external attacks," often suffering from slow response, heavy communication burdens, and insufficient security robustness. Therefore, there is an urgent need to develop a novel intelligent control strategy capable of collaboratively addressing multiple uncertainties. Summary of the Invention

[0004] This disclosure provides at least one microgrid finite-time control method and apparatus with an event-triggered mechanism. By introducing an event-triggered mechanism and designing a finite-time cooperative control law, it achieves significant savings in system communication resources while ensuring that the microgrid can quickly and cooperatively restore frequency stability under new energy fluctuations and network attacks, thereby effectively improving the dynamic performance and operational robustness of the power system.

[0005] This disclosure provides a finite-time control method for a microgrid with an event-triggered mechanism, including: Establish a microgrid system model that includes multiple distributed power sources; For each distributed power source in the microgrid system model, based on the real-time frequency state data of the distributed power source, the frequency tracking error and state estimation error corresponding to the distributed power source are determined. Based on the frequency tracking error and state estimation error of each distributed power source, it is determined whether each distributed power source meets the event triggering conditions. In response to any of the distributed power sources satisfying the event triggering condition, a finite-time cooperative control law corresponding to each distributed power source is generated based on the control update data received from each distributed power source when the event triggering condition is satisfied. For each distributed power source in the microgrid system model, a control command is generated according to the finite-time cooperative control law, and the distributed power source is subjected to finite-time frequency recovery control based on the control command.

[0006] In some possible embodiments, establishing a microgrid system model comprising multiple distributed power sources includes: For each distributed power source, a new energy disturbance term corresponding to the distributed power source is introduced into the dynamic equation of the microgrid system model, and the output of the new energy disturbance term corresponding to each distributed power source is set to be bounded; wherein, the new energy disturbance term is used to characterize the random power output fluctuation of new energy generation. An attack-affected term is introduced into the dynamic equations of the microgrid system model; wherein, the attack-affected term is used to characterize the disruptive state caused by denial-of-service attacks to the coordinated control process of each distributed power source; The dynamic equations of the microgrid system model are expressed as follows: ; ; in, This represents the effective control input of the i-th distributed power source at time t, which is affected by a denial-of-service attack. This is represented as the control input of the i-th distributed power source at time t; This indicates the fluctuation state of new energy sources. This indicates the existence of fluctuations in new energy sources. This indicates that there is no fluctuation in new energy sources; Let represent the new energy disturbance term of the i-th distributed power source at time t; Represented as a new energy disturbance term The output has a bounded value.

[0007] In some possible embodiments, determining the frequency tracking error and state estimation error corresponding to the distributed power source includes: For each distributed power source, the difference between the real-time frequency status data of the distributed power source and the reference standard frequency is calculated to obtain the frequency tracking error; For each distributed power source, the state estimation error is calculated based on the real-time frequency state data of the distributed power source and the historical frequency value at the time of the most recent event trigger.

[0008] In some possible embodiments, determining whether each distributed power source meets the event triggering condition based on the frequency tracking error and state estimation error of each distributed power source includes: In the microgrid system model, each distributed power source maintains a neighbor state cache, which is used to store the most recent control update data received from each neighboring distributed power source; wherein, the control update data includes the historical frequency values ​​of the neighboring distributed power sources of the distributed power source at the time of the most recent event triggering. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated based on its own frequency tracking error and the error between the historical frequency values ​​of each adjacent distributed power source corresponding to the distributed power source and the reference standard frequency obtained from the neighbor state cache. For each distributed power source, determine whether the absolute value of the state estimation error is not less than the product of a preset threshold and the absolute value of the asynchronous neighborhood tracking frequency error; If the determination is yes, then the distributed power source is determined to meet the event triggering condition.

[0009] In some possible embodiments, generating a finite-time cooperative control law corresponding to each distributed power source based on control update data received from each distributed power source when the event triggering condition is met includes: When it is determined that any of the distributed power sources meets the event triggering condition, the distributed power source is used as a reference distributed power source, and the control update data is sent to each of the adjacent distributed power sources corresponding to the reference distributed power source. In the microgrid system model, after receiving the control update data sent by its neighboring distributed power sources, any distributed power source updates the neighbor state cache of that distributed power source. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated and updated based on its own frequency tracking error and the error between the historical frequency value and the reference standard frequency contained in the control update data at the most recent event trigger time of each neighboring distributed power source obtained from the updated neighbor state cache. For each distributed power source, a finite-time cooperative control law for the distributed power source is calculated and generated based on the updated asynchronous neighborhood tracking frequency error of the distributed power source and a preset feedback control function.

[0010] In some possible embodiments, the preset feedback control function is expressed as: ; ; in, This represents the control input of the i-th distributed power source at time t, i.e., the finite-time cooperative control law corresponding to the distributed power source. This represents the control gain coefficient of the first feedback term in the control law, used to adjust the strength of the first feedback term. ; This is represented as the control gain coefficient of the second feedback term in the control law, used to overcome the bounded influence of new energy disturbances. a and b represent the first and second preset coefficients, respectively, and are positive odd numbers. ; Represented as a symbolic function; It is represented as the asynchronous neighborhood tracking frequency error of the i-th distributed power source at time t when the k-th event is triggered; It is represented as the frequency measurement value of the i-th distributed power source at time t when the k-th event is triggered; This represents the frequency value of the most recent event triggering time of the i-th distributed power source, which is the neighboring distributed power source of the j-th distributed power source, at time t when the k-th event is triggered. Represented as the reference standard frequency; It is represented as the set of adjacent distributed power sources that are directly connected to the i-th distributed power source; It represents the weight coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when there is no denial-of-service attack. It is represented as the pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when there is no denial-of-service attack; This represents the state where the communication link between the i-th distributed power source and the j-th distributed power source is under attack; It represents the time-varying weighting coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when subjected to a denial-of-service attack; It is represented as the time-varying pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when subjected to a denial-of-service attack; This represents the set of time intervals in which denial-of-service attacks do not exist. This represents the set of time intervals in which a denial-of-service attack exists.

[0011] In some possible embodiments, calculating and updating the asynchronous neighborhood tracking frequency error of the distributed power source includes: The adjacency matrix and Laplace matrix of the distributed power source are determined based on the communication topology of the microgrid system model. Monitor the impact on the communication links of the distributed power source under denial-of-service attacks; When a denial-of-service attack is detected that causes a change in the communication topology of the distributed power source, the adjacency matrix and Laplace matrix used to calculate the asynchronous neighborhood tracking frequency error are switched to matrices corresponding to the communication topology after the attack.

[0012] This disclosure provides a microgrid finite-time control device with an event-triggered mechanism, comprising: The model building module is used to build a microgrid system model that includes multiple distributed power sources; The error calculation module is used to determine the frequency tracking error and state estimation error corresponding to each distributed power source in the microgrid system model based on the real-time frequency state data of the distributed power source. The triggering judgment module is used to determine whether each distributed power source meets the event triggering conditions based on the frequency tracking error and state estimation error of each distributed power source. A control generation module is configured to, in response to any of the distributed power sources satisfying the event triggering condition, generate a finite-time cooperative control law corresponding to each distributed power source based on control update data received from each distributed power source when the event triggering condition is satisfied. The control recovery module is used to generate control commands for each distributed power source in the microgrid system model according to the finite-time cooperative control law, and to perform finite-time frequency recovery control on the distributed power source based on the control commands.

[0013] In some possible embodiments, the model building module is specifically used for: For each distributed power source, a new energy disturbance term corresponding to the distributed power source is introduced into the dynamic equation of the microgrid system model, and the output of the new energy disturbance term corresponding to each distributed power source is set to be bounded; wherein, the new energy disturbance term is used to characterize the random power output fluctuation of new energy generation. An attack-affected term is introduced into the dynamic equations of the microgrid system model; wherein, the attack-affected term is used to characterize the disruptive state caused by denial-of-service attacks to the coordinated control process of each distributed power source; The dynamic equations of the microgrid system model are expressed as follows: ; ; in, This represents the effective control input of the i-th distributed power source at time t, which is affected by a denial-of-service attack. This is represented as the control input of the i-th distributed power source at time t; This indicates the fluctuation state of new energy sources. This indicates the existence of fluctuations in new energy sources. This indicates that there is no fluctuation in new energy sources; Let represent the new energy disturbance term of the i-th distributed power source at time t; Represented as a new energy disturbance term The output has a bounded value.

[0014] In some possible embodiments, the error calculation module is specifically used for: For each distributed power source, the difference between the real-time frequency status data of the distributed power source and the reference standard frequency is calculated to obtain the frequency tracking error; For each distributed power source, the state estimation error is calculated based on the real-time frequency state data of the distributed power source and the historical frequency value at the time of the most recent event trigger.

[0015] In some possible embodiments, the trigger determination module is specifically used for: In the microgrid system model, each distributed power source maintains a neighbor state cache, which is used to store the most recent control update data received from each neighboring distributed power source; wherein, the control update data includes the historical frequency values ​​of the neighboring distributed power sources of the distributed power source at the time of the most recent event triggering. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated based on its own frequency tracking error and the error between the historical frequency values ​​of each adjacent distributed power source corresponding to the distributed power source and the reference standard frequency obtained from the neighbor state cache. For each distributed power source, determine whether the absolute value of the state estimation error is not less than the product of a preset threshold and the absolute value of the asynchronous neighborhood tracking frequency error; If the determination is yes, then the distributed power source is determined to meet the event triggering condition.

[0016] In some possible embodiments, the control generation module is specifically used for: When it is determined that any of the distributed power sources meets the event triggering condition, the distributed power source is used as a reference distributed power source, and the control update data is sent to each of the adjacent distributed power sources corresponding to the reference distributed power source. In the microgrid system model, after receiving the control update data sent by its neighboring distributed power sources, any distributed power source updates the neighbor state cache of that distributed power source. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated and updated based on its own frequency tracking error and the error between the historical frequency value and the reference standard frequency contained in the control update data at the most recent event trigger time of each neighboring distributed power source obtained from the updated neighbor state cache. For each distributed power source, a finite-time cooperative control law for the distributed power source is calculated and generated based on the updated asynchronous neighborhood tracking frequency error of the distributed power source and a preset feedback control function.

[0017] In some possible embodiments, the preset feedback control function is expressed as: ; ; in, This represents the control input of the i-th distributed power source at time t, i.e., the finite-time cooperative control law corresponding to the distributed power source. This represents the control gain coefficient of the first feedback term in the control law, used to adjust the strength of the first feedback term. ; This is represented as the control gain coefficient of the second feedback term in the control law, used to overcome the bounded influence of new energy disturbances. a and b represent the first and second preset coefficients, respectively, and are positive odd numbers. ; Represented as a symbolic function; It is represented as the asynchronous neighborhood tracking frequency error of the i-th distributed power source at time t when the k-th event is triggered; It is represented as the frequency measurement value of the i-th distributed power source at time t when the k-th event is triggered; This represents the frequency value of the most recent event triggering time of the i-th distributed power source, which is the neighboring distributed power source of the j-th distributed power source, at time t when the k-th event is triggered. Represented as the reference standard frequency; It is represented as the set of adjacent distributed power sources that are directly connected to the i-th distributed power source; It represents the weight coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when there is no denial-of-service attack. It is represented as the pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when there is no denial-of-service attack; This represents the state where the communication link between the i-th distributed power source and the j-th distributed power source is under attack; It represents the time-varying weighting coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when subjected to a denial-of-service attack; It is represented as the time-varying pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when subjected to a denial-of-service attack; This represents the set of time intervals in which denial-of-service attacks do not exist. This represents the set of time intervals in which a denial-of-service attack exists.

[0018] In some possible embodiments, the control generation module is specifically used for: The adjacency matrix and Laplace matrix of the distributed power source are determined based on the communication topology of the microgrid system model. Monitor the impact on the communication links of the distributed power source under denial-of-service attacks; When a denial-of-service attack is detected that causes a change in the communication topology of the distributed power source, the adjacency matrix and Laplace matrix used to calculate the asynchronous neighborhood tracking frequency error are switched to matrices corresponding to the communication topology after the attack.

[0019] This disclosure provides a computer device, including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, a microgrid finite-time control method with an event-triggered mechanism as described in any of the above possible embodiments is executed.

[0020] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a microgrid finite-time control method with an event-triggered mechanism as described in any of the possible embodiments above.

[0021] The microgrid finite-time control method and apparatus with event-triggered mechanism provided in this disclosure introduces an event-triggered mechanism, which triggers communication and control updates only when the frequency tracking error and state estimation error of the distributed power source meet specific conditions, thereby significantly reducing the communication burden of the system. By generating and executing a finite-time cooperative control law, it can ensure that all distributed power sources can quickly and cooperatively achieve frequency recovery within a preset finite time, effectively coping with the dual interference caused by fluctuations in new energy output and network attacks. Ultimately, while saving communication resources, it significantly improves the dynamic response speed, control accuracy, and operational safety and robustness of the microgrid system.

[0022] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0024] Figure 1 A flowchart of a microgrid finite-time control method with an event-triggered mechanism provided by an embodiment of this disclosure is shown; Figure 2 A flowchart of a finite-time cooperative control law generation method provided by an embodiment of this disclosure is shown; Figure 3 A flowchart of a microgrid communication topology adjustment method provided in an embodiment of this disclosure is shown; Figure 4 A schematic diagram of a microgrid communication topology change provided by an embodiment of this disclosure is shown; Figure 5 A schematic diagram of the structure of a microgrid finite-time control device with an event-triggered mechanism provided in an embodiment of this disclosure is shown. Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0027] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0028] Against the backdrop of the current energy structure transformation, with the continuous increase in energy demand and the enhancement of environmental awareness, new energy sources, as a clean and sustainable energy type, play a crucial role in optimizing my country's energy structure. In particular, distributed generation (DG), with its advantages of low investment costs and efficient energy utilization, has been widely promoted and applied in microgrid systems.

[0029] However, distributed generation (DG) systems often experience power output fluctuations during actual operation. These fluctuations are typically irregular and difficult to predict, posing a serious challenge to the stability of microgrid systems. Furthermore, the rapid development of information technology has exposed microgrid systems to increasing cybersecurity threats, such as denial-of-service (DoS) attacks. These cyberattacks can not only cause malfunctions in microgrid systems but may even trigger serious security incidents. Traditional control strategies often fail to effectively address these uncertainties and potential attacks.

[0030] Research has revealed several shortcomings in existing technologies to address these challenges: First, the increasing proportion of new energy sources in power systems in recent years has led to fluctuations in power generation, negatively impacting power quality. However, many studies have not adequately considered the impact of these fluctuations on microgrids. Second, as typical cyber-physical systems, microgrids are vulnerable to cyberattacks, such as DoS attacks. Current research on microgrid systems often overlooks these attack risks, hindering in-depth analysis of practical problems. Finally, as microgrids expand, communication resources become limited. Effectively conserving communication resources under cyberattack conditions has become a critical issue requiring resolution. Therefore, combining event-triggered mechanisms to effectively control disturbances and attacks from distributed new energy sources to ensure the stable operation of microgrid systems has become a current research hotspot.

[0031] Based on the above research, this disclosure provides a microgrid finite-time control method and apparatus with an event-triggered mechanism. It establishes a microgrid system model and monitors the frequency status of each distributed power source in real time, comprehensively calculating its frequency tracking error and state estimation error, using this as the basis for determining the event triggering condition. When any distributed power source meets the event triggering condition, it immediately collects the control update data of each power source at that moment and generates a corresponding finite-time cooperative control law. Finally, it issues control commands to each distributed power source according to the control law to achieve cooperative frequency recovery control.

[0032] In this embodiment, by introducing an event-triggered mechanism, communication and control updates are triggered only when the frequency tracking error and state estimation error of the distributed power source meet specific conditions, thereby significantly reducing the communication burden of the system. Furthermore, by generating and executing a finite-time cooperative control law, it is possible to ensure that all distributed power sources can quickly and cooperatively achieve frequency recovery within a preset finite time, effectively addressing the dual interference caused by fluctuations in new energy output and network attacks. Ultimately, while saving communication resources, it significantly improves the dynamic response speed, control accuracy, and operational safety and robustness of the microgrid system.

[0033] To facilitate understanding of this embodiment, the executing entity of the microgrid finite-time control method with event-triggered mechanism provided in this disclosure embodiment will first be described in detail. The executing entity of the microgrid finite-time control method with event-triggered mechanism provided in this disclosure embodiment is a computer device. This computer device can be a terminal device or a server. The terminal device can also be a mobile device, user terminal, terminal, handheld device, computing device, vehicle-mounted device, wearable device, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of computer devices and servers.

[0034] The finite-time control method for microgrids with an event-triggered mechanism provided in this application, as illustrated in the accompanying drawings, will be described in detail below. (See also...) Figure 1 The diagram shows a flowchart of a microgrid finite-time control method with an event-triggered mechanism provided in an embodiment of this disclosure. The method includes the following steps S101 to S105: S101, Establish a microgrid system model that includes multiple distributed power sources.

[0035] As is understandable, distributed generation refers to small and medium-sized power generation units that are distributed across the user side or distribution network, such as photovoltaic panels, wind turbines, fuel cells, or energy storage devices. They are typically connected to a microgrid via power electronic converters to achieve local power generation and consumption. By establishing a mathematical model, the dynamic behavior of each distributed generation in the power system and its interaction with the network can be described.

[0036] Specifically, when establishing a microgrid system model, differential equations reflecting changes in electrical quantities such as frequency, voltage, and power can be constructed based on circuit laws and motor motion equations. During the modeling process, due to the intermittent and stochastic nature of renewable energy generation, for each distributed power source, in addition to considering its own control dynamics and load characteristics, a renewable energy disturbance term corresponding to the distributed power source can be introduced into the dynamic equations of the microgrid system model, and the output boundedness of the renewable energy disturbance term corresponding to each distributed power source can be set. The dynamic equations of the microgrid system model are used to characterize the evolution of the system state over time; the renewable energy disturbance term is used to characterize the random power output fluctuations of renewable energy generation, specifically manifested as the fluctuations in photovoltaic or wind turbine output power caused by instantaneous changes in sunlight intensity, random fluctuations in wind speed, etc. Output boundedness means that the amplitude of the disturbance term varies within a certain range; for example, its absolute value can be set to be less than a known positive real number (such as an upper limit determined based on historical data statistics or actual engineering experience, assuming it is M, i.e., ...). This reflects the physical limitations of actual new energy output.

[0037] Furthermore, considering the cybersecurity threats faced by microgrids as cyber-physical systems, this disclosure proposes to introduce an attack-affected term into the dynamic equations of the microgrid system model. The attack-affected term is used to characterize the destructive state caused by denial-of-service attacks on the coordinated control process of each distributed power source. For example, attackers can block or flood communication channels, causing the state information between distributed power sources or between distributed power sources and the controller to be unable to be transmitted normally, thereby disrupting the original coordinated control law, causing the neighbor information on which frequency regulation depends to be interrupted or delayed, and thus affecting the overall stability of the system.

[0038] Here, the dynamic equations of the microgrid system model can be expressed as: ; ; in, This represents the effective control input of the i-th distributed power source at time t, which is affected by a denial-of-service attack. This is represented as the control input of the i-th distributed power source at time t; This indicates the fluctuation state of new energy sources. This indicates the existence of fluctuations in new energy sources. This indicates that there is no fluctuation in new energy sources; Let represent the new energy disturbance term of the i-th distributed power source at time t; Represented as a new energy disturbance term The output has a bounded value.

[0039] S102, for each distributed power source in the microgrid system model, based on the real-time frequency state data of the distributed power source, determine the frequency tracking error and state estimation error corresponding to the distributed power source.

[0040] Specifically, real-time frequency status data refers to the instantaneous operating frequency value of the system collected by frequency measurement devices installed locally or on critical buses of each distributed power source. This data exists in the form of continuous or discrete time series and can be used to monitor the operating status of the power source node in real time, including the actual measured value of the frequency or its filtered signal.

[0041] Here, for each distributed generation in the microgrid system model, after obtaining its real-time frequency state data, the frequency tracking error and state estimation error corresponding to that distributed generation can be determined. The frequency tracking error refers to the deviation between the real-time operating frequency of the distributed generation and the reference standard frequency required to be maintained by the entire microgrid system, directly reflecting the degree of frequency deviation of that node. The state estimation error refers to the difference between the current real-time frequency measurement value of the distributed generation and its own frequency estimate reconstructed or predicted based on the historical state information of neighboring distributed generation sources recorded in its local cache. This error is used to measure the inconsistency between the local true state and the locally perceived state constructed based on asynchronous neighbor information.

[0042] For example, calculating the frequency tracking error and the state estimation error may include the following steps (1) to (2): (1) For each distributed power source, calculate the difference between the real-time frequency status data of the distributed power source and the reference standard frequency to obtain the frequency tracking error; (2) For each distributed power source, the state estimation error is calculated based on the real-time frequency state data of the distributed power source and the historical frequency value of the most recent event trigger time of the distributed power source.

[0043] It is understandable that the reference standard frequency refers to the rated frequency that the microgrid system needs to maintain under steady-state conditions, such as 50 Hz or 60 Hz; it is the unified target for frequency control. By calculating the algebraic difference between the real-time operating frequency of each distributed power source and the reference standard frequency, the degree to which the frequency of that node deviates from the target can be obtained, i.e., the frequency tracking error.

[0044] Here, the historical frequency value at the moment of the most recent event trigger of the distributed power source can be stored in the local non-volatile memory of the distributed power source, such as its controller's internal register or dedicated storage unit. The historical frequency value is a snapshot of the distributed power source's own frequency recorded synchronously when it last met the triggering conditions and broadcast its status. By calculating the algebraic difference between the current real-time frequency and the locally stored historical frequency value, the state estimation error can be obtained. This error reflects the cumulative change in the distributed power source's own frequency state since its last active communication, and is the core basis for determining whether its current state has deviated significantly, and thus whether a new round of communication and control updates needs to be initiated.

[0045] For example, frequency tracking error can be expressed by the formula The calculation yields the following result: Let be the frequency tracking error of the i-th distributed source at time t. This represents the real-time frequency status data of the i-th distributed power source. This is expressed as the reference standard frequency. The state estimation error can be expressed by the formula... In the formula, Let be the state estimation error of the i-th distributed source at time t. Let be the historical frequency value of the i-th distributed power source at the k-th trigger moment.

[0046] S103, based on the frequency tracking error and state estimation error of each distributed power source, determine whether each distributed power source meets the event triggering condition.

[0047] Furthermore, after obtaining the frequency tracking error and state estimation error corresponding to each distributed power source, these two error quantities can be combined with its neighbor state information for comprehensive evaluation to determine whether the distributed power source meets the event triggering condition at the current moment. The event triggering condition is a preset logical decision rule designed to minimize unnecessary communication data flow between distributed power sources while ensuring frequency recovery control performance. By comparing the degree of change in the state of the distributed power source itself (i.e., state estimation error) with a dynamic threshold reflecting the overall tracking deviation of the system, it can be determined whether it is necessary to trigger an event, update control commands, and notify neighboring nodes.

[0048] Here, in the microgrid system model, each distributed generation maintains a neighbor state cache, which stores the most recent control update data received from each neighboring distributed generation. This control update data includes the historical frequency values ​​of the neighboring distributed generation at the time of its most recent event trigger.

[0049] In some other embodiments, the control update data may also include timestamp information to identify the specific event trigger time corresponding to the historical frequency value. The receiving distributed power source can assess the timeliness of the received data based on the timestamp information and can be used to implement more complex cache management and data validity judgment strategies. Furthermore, the control update data may also contain a unique identifier of the sending distributed power source to ensure that the receiver can correctly update the corresponding entry in its neighbor state cache.

[0050] As can be understood, neighboring distributed power sources refer to other distributed power sources directly connected to the current distributed power source via a communication network, capable of exchanging state information. Examples include a group of photovoltaic inverters and energy storage converters interconnected within the same microgrid via wired or wireless communication links. The event trigger time refers to the specific point in time when the neighboring distributed power source last met its local event triggering conditions and thus actively broadcast its state data to the network. Each distributed power source maintains a neighbor state cache to record the frequency of the most recent broadcasts from its neighboring distributed power sources. In this way, each distributed power source can obtain an asynchronous, non-real-time cognitive picture of the neighboring node states without continuous real-time communication.

[0051] Specifically, determining whether each distributed power source meets the event triggering conditions may include the following steps (a) to (c): (a) For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated based on the frequency tracking error of the distributed power source itself and the error between the historical frequency value of each adjacent distributed power source corresponding to the distributed power source and the reference standard frequency obtained from the neighbor state cache. (b) For each distributed power source, determine whether the absolute value of the state estimation error is not less than the product of a preset threshold and the absolute value of the asynchronous neighborhood tracking frequency error; (c) If the determination is yes, then the distributed power source is determined to meet the event triggering condition.

[0052] It is understandable that the historical frequency values ​​of each neighboring distributed power source (DPG) obtained from the neighbor state cache are data broadcast by each neighbor node at the time of its last triggered event. These are asynchronous and may not be the latest information. This historical data can be used to assess the coordination deviation between the DPG and its neighboring nodes. By comprehensively weighting the DPG's own real-time frequency tracking error with the neighbor historical frequency values ​​extracted from the cache and the reference standard frequency, according to the system's communication topology weights, a composite error signal can be obtained, namely the asynchronous neighborhood tracking frequency error. This error can comprehensively reflect the degree to which the local node's frequency deviates from the target and the coordination difference between its historical state and that of its neighboring nodes.

[0053] Here, after obtaining the asynchronous neighborhood tracking frequency error, its absolute value can be multiplied by a preset threshold to form a dynamically changing trigger threshold. Subsequently, the absolute value of the state estimation error, representing the local state change, is compared with this dynamic threshold to determine whether the cumulative change in the local state since the last communication has exceeded the allowable range under the current system conditions, thus determining whether a new event needs to be triggered immediately to update control and communication. The preset threshold is an adjustable design parameter greater than zero, which can be set through system stability analysis, simulation debugging, or empirical formulas. For example, it can be set to a decimal between 0 and 1 to adjust the balance between the sensitivity of the event triggering mechanism and the communication frequency.

[0054] Specifically, the event triggering condition can be represented by the following formula: ; ; in, Let t represent the trigger time of the next (k+1) event for the i-th distributed power source, which is determined by the current time t satisfying the triggering condition. The decision was made at that time. This represents the earliest (infimum) time that satisfies the condition; This is represented as an event triggering judgment function; This represents the time when the k-th event is triggered by the i-th distributed power source; It is represented as the asynchronous neighborhood tracking frequency error of the i-th distributed power source at time t when the k-th event is triggered; Represented as a preset threshold, it is an adjustable design parameter used to adjust the sensitivity of event triggering conditions. The larger the value, the stricter the triggering conditions (the less likely to be triggered) and the sparser the communication; the smaller the value, the more lenient the triggering conditions and the more frequent the communication. This is expressed as the maximum allowable interval time, preventing prolonged periods of communication when system state changes are extremely slow (avoiding "deadlock"), and ensuring that even if the state error does not exceed the threshold, communication will resume after the maximum time interval has elapsed. It will also force an event to be triggered once afterward, which is a security mechanism to ensure the basic communication and stability of the system.

[0055] S104, in response to any of the distributed power sources satisfying the event triggering condition, a finite-time cooperative control law corresponding to each distributed power source is generated based on the control update data received from each distributed power source when the event triggering condition is satisfied.

[0056] Specifically, after the event triggering logic of any distributed power source passes the judgment, in response to the distributed power source being determined to meet its event triggering conditions, the distributed power source node generates and broadcasts its control update data according to a preset process. Simultaneously, other distributed power sources in the network continuously monitor the communication link, receiving and processing such update data sent from their neighboring nodes. This data serves as new input, triggering the receiver to update its internal state and recalculate control commands, thereby obtaining the latest finite-time cooperative control law applicable to each power source node itself.

[0057] Here, the finite-time cooperative control law refers to a distributed control algorithm based on fractional power feedback. Its design goal is to ensure that the frequency of the microgrid system can converge to the reference standard frequency within a certain and finite time interval after being disturbed or attacked, rather than the asymptotic convergence under the traditional control strategy, thereby providing faster and more accurate frequency recovery performance.

[0058] In some possible embodiments, when generating a finite-time cooperative control law in response to any distributed source satisfying the event triggering condition, reference is made to... Figure 2 As shown, the steps S201~S204 may be included: S201, when it is determined that any of the distributed power sources meets the event triggering condition, the any of the distributed power sources is used as a reference distributed power source, and the control update data is sent to each of the adjacent distributed power sources corresponding to the reference distributed power source.

[0059] Understandably, when a distributed power source meets the event triggering condition, its current moment can be recorded as its new event triggering moment. The frequency value measured at this moment can be encapsulated as control update data, and then broadcast to all its neighboring nodes through its communication port, thus completing an event-driven data distribution. For example, consider a simple microgrid containing three distributed power sources (DG1, DG2, DG3), with communication connections between DG1 and DG2 and between DG2 and DG3. Assume that at t=15 seconds, DG2, based on its locally calculated frequency tracking error and state estimation error, determines that its state change has exceeded the dynamic threshold modulated by the asynchronous neighborhood tracking frequency error, thus meeting the event triggering condition. At this point, DG2 immediately performs the following operations: First, it records t=15 seconds as its new latest event trigger time; next, it reads the current real-time frequency value measured by its local frequency sensor, for example, 49.85 Hz; then, it encapsulates this frequency value along with its own identifier and timestamp into a structured data packet; finally, DG2 broadcasts this data packet simultaneously to all its directly connected neighboring distributed power sources (i.e., DG1 and DG3) through its communication module. This process is completed spontaneously in the distributed system, without the need for central controller scheduling. For DG1 and DG3, they are passive receivers of this event. After listening to the broadcast data from DG2 on the network, they will trigger the subsequent cache update and control law recalculation process.

[0060] S202, in the microgrid system model, after receiving the control update data sent by its neighboring distributed power sources, any distributed power source updates the neighbor state cache of that distributed power source.

[0061] Understandably, the neighbor state cache is a data structure used locally by each distributed power source to store the state information of its neighboring nodes. When a distributed power source receives a control update data packet from a neighbor, it parses the packet to obtain the neighbor's identifier and its latest frequency value. Subsequently, the distributed power source can look up the entry corresponding to the neighbor identifier in its local neighbor state cache and update the frequency value stored in that entry with the newly received data. Furthermore, after updating the corresponding entry in the neighbor state cache, the receiving distributed power source obtains the latest historical state information about that specific neighbor node.

[0062] S203, for each distributed power source, calculate and update the asynchronous neighborhood tracking frequency error of the distributed power source based on its own frequency tracking error and the error between the historical frequency value and the reference standard frequency contained in the control update data at the most recent event triggering time of each adjacent distributed power source obtained from the updated neighbor state cache.

[0063] Here, the computation process of each distributed power source is independent and distributed. Each distributed power source can utilize the computing unit in its own controller to calculate a comprehensive quantity representing the current node's coordination deviation with its neighbors, namely the updated asynchronous neighborhood tracking frequency error, based on a preset algorithm formula, combining the local tracking error and the asynchronous neighbor's historical tracking error. Specifically, the controller first calculates the difference between its own real-time frequency and the reference standard frequency to obtain its own frequency tracking error. Simultaneously, the controller queries its updated neighbor state cache, extracts the stored frequency value for each neighbor node recorded in the cache, calculates the difference between this value and the reference standard frequency, and obtains the historical information-based tracking error for each neighbor. Finally, the controller combines its own frequency tracking error with the historical tracking errors of all neighbors according to preset rules (e.g., weighted summation based on communication topology), and the result is the current asynchronous neighborhood tracking frequency error of the distributed power source.

[0064] In some possible embodiments, since microgrid systems may be subject to denial-of-service attacks, which could cause partial communication link failures and alter the system's communication topology, the calculation and updating of the asynchronous neighborhood tracking frequency error of distributed generation also needs to consider the currently valid communication connections. This requires dynamically adjusting the topology parameters upon which the error calculation depends, referring to... Figure 3 As shown, the steps S301 to S303 may be included: S301, determine the adjacency matrix and Laplace matrix of the distributed power source based on the communication topology of the microgrid system model.

[0065] Here, the adjacency matrix is ​​a graph theory matrix used to describe the connections between nodes. It can be constructed based on the communication link information between distributed sources in the system, either pre-defined or dynamically established. The Laplace matrix is ​​a matrix calculated from the adjacency matrix and the degree matrix. It is often used to analyze the consistency characteristics of a network and can be expressed as the degree matrix minus the adjacency matrix. By obtaining the correct adjacency matrix and Laplace matrix, the information interaction relationships between nodes can be accurately formalized.

[0066] S302, Monitor the impact on the communication link of the distributed power source under the condition of a denial-of-service attack.

[0067] Understandably, a denial-of-service attack is a type of cyberattack that disrupts normal communication by overwhelming the target's communication channels or exhausting the resources of the target node. During an attack, communication links between some distributed power sources may be interrupted. In this case, monitoring mechanisms deployed at the communication or application layer can detect changes in the connectivity of these links in real-time or near real-time, thereby determining whether the current communication topology has been compromised.

[0068] S303, when a denial-of-service attack is detected that causes a change in the communication topology of the distributed power source, the adjacency matrix and Laplace matrix used to calculate the asynchronous neighborhood tracking frequency error are switched to matrices corresponding to the communication topology after the attack.

[0069] Furthermore, after detecting link state changes and completing matrix switching, all distributed power sources will automatically perform weighted summation based on the new matrix corresponding to the actual communication topology after the attack when calculating their asynchronous neighborhood tracking frequency error. This ensures that even if some communication is blocked, the cooperative control algorithm can still continue to work based on the remaining effective communication network. (Refer to...) Figure 4 As shown in the figure, taking a microgrid containing four distributed power sources (DG1, DG2, DG3, DG4) as an example, G0 represents the original topology when all communication links are normal and no attack occurs; G1 represents the communication topology after an attacker launches an attack on a specific link, causing some connections in the figure to be interrupted; G2 represents the communication topology formed after the attack mode changes and another set of communication links is damaged.

[0070] S204, for each distributed power source, based on the updated asynchronous neighborhood tracking frequency error of the distributed power source and the preset feedback control function, calculate and generate the finite-time cooperative control law of the distributed power source.

[0071] Specifically, after obtaining the updated asynchronous neighborhood tracking frequency error, the error value can be substituted into a preset feedback control function that includes sign function and fractional power operation. Then, the control input that the distributed power source should apply can be calculated, i.e., the finite-time cooperative control law, so as to achieve precise regulation of the local power unit (such as the inverter) and drive the system frequency to recover stability within a finite time.

[0072] Here, the preset feedback control function can be expressed as: ; ; in, This represents the control input of the i-th distributed power source at time t, i.e., the finite-time cooperative control law corresponding to the distributed power source. This represents the control gain coefficient of the first feedback term in the control law, used to adjust the strength of the first feedback term. ; This is represented as the control gain coefficient of the second feedback term in the control law, used to overcome the bounded influence of new energy disturbances. a and b represent the first and second preset coefficients, respectively, which are the numerator and denominator of the fractional power exponent, and are positive odd numbers. ; Represented as a symbolic function; It is represented as the asynchronous neighborhood tracking frequency error of the i-th distributed power source at time t when the k-th event is triggered; It is represented as the frequency measurement value of the i-th distributed power source at time t when the k-th event is triggered; This represents the frequency value of the most recent event triggering time of the i-th distributed power source, which is the neighboring distributed power source of the j-th distributed power source, at time t when the k-th event is triggered. Represented as the reference standard frequency; It is represented as the set of adjacent distributed power sources that are directly connected to the i-th distributed power source; It represents the weight coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when there is no denial-of-service attack. It is represented as the pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when there is no denial-of-service attack; This represents the state where the communication link between the i-th distributed power source and the j-th distributed power source is under attack; It represents the time-varying weighting coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when subjected to a denial-of-service attack; It is represented as the time-varying pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when subjected to a denial-of-service attack; This represents the set of time intervals in which denial-of-service attacks do not exist. This represents the set of time intervals in which a denial-of-service attack exists.

[0073] S105, for each distributed power source in the microgrid system model, generate control commands according to the finite-time cooperative control law, and perform finite-time frequency recovery control on the distributed power source based on the control commands.

[0074] Understandably, after obtaining the finite-time coordinated control law independently calculated for each distributed power source, the local controller of each distributed power source in the microgrid system model can convert the mathematical output value of the control law into specific, executable control commands. This conversion process may involve dimensional conversion, signal modulation, and drive interface adaptation. For example, for a distributed photovoltaic power source connected to the grid via an inverter, its control law calculation result (a voltage or power reference value representing the required additional control input) will be sent to the inverter's pulse width modulation module to generate corresponding switching transistor drive signals. Based on this series of generated control commands, the power execution unit of the distributed power source (such as an inverter, energy storage converter, or controllable generator) can further adjust its active power output to actively offset power deficits or surpluses in the system, thereby directly affecting the frequency dynamics of the grid connection point.

[0075] In this way, the above closed-loop control process forces the output of each distributed power source to reach a new power balance with the load and disturbance, driving the frequency of the entire microgrid system to start from the disturbed state and quickly and accurately recover to the rated reference value along the preset finite-time convergence trajectory. This achieves robust, coordinated and finite-time stability of the system frequency in a complex environment where there are random disturbances from new energy sources and intermittent denial-of-service attacks.

[0076] To demonstrate the effectiveness of the microgrid finite-time control method with event-triggered mechanism proposed in this disclosure, this disclosure also employs Lyapunov stability theory and finite-time convergence analysis to rigorously prove the dynamic performance of the closed-loop control system. This proves that under the conditions of simultaneous random disturbances from new energy sources and denial-of-service attacks, the system frequency can converge to the reference standard frequency within a preset finite time, and the designed event-triggered mechanism can effectively avoid Zeno behavior of infinitely frequent communication.

[0077] Specifically, in the verification process, firstly, a Lyapunov candidate function based on composite cooperative error is constructed. This function is a positive definite and radially unbounded scalar function, suitable as a stability criterion. Then, the derivative of the Lyapunov function along the system trajectory is calculated, and its derivative is analyzed in conjunction with the proposed finite-time cooperative control law and event triggering conditions. The analysis distinguishes between two scenarios: normal system communication and a denial-of-service attack. During periods of normal communication, by substituting into the control law and utilizing the properties of algebraic inequalities, it can be derived that the derivative of the Lyapunov function satisfies a negative definite inequality, which implies the finite-time convergence of the system state. During periods of denial-of-service attack, due to the interruption of some communication links caused by the attack, the system's communication topology changes, and the Laplace matrix in the corresponding cooperative error calculation needs to be switched to a time-varying matrix representing the currently effective connections.

[0078] Furthermore, analysis shows that in this case, the derivative of the Lyapunov function is not greater than zero, ensuring that the system state will not diverge. Considering both attack and non-attack intervals, through integral calculations, it can be rigorously proven that under the coordinated control of all distributed power sources, the frequency tracking error of the entire microgrid system can converge to zero within a finite time, and the upper bound of the convergence time can be calculated. In addition, regarding the designed event triggering mechanism, by analyzing the lower bound of the triggering function, it can be proven that there must be a positive time interval between any two triggering events, thus eliminating Zeno behavior of infinite triggers within a finite time and ensuring the engineering feasibility of the scheme. The above theoretical analysis collectively confirms that this method can ensure the finite-time recovery of the microgrid frequency in an environment where new energy disturbances and intermittent denial-of-service attacks coexist, while significantly saving communication resources.

[0079] The microgrid finite-time control method and apparatus with event-triggered mechanism provided in this disclosure introduces an event-triggered mechanism, which triggers communication and control updates only when the frequency tracking error and state estimation error of the distributed power source meet specific conditions, thereby significantly reducing the communication burden of the system. By generating and executing a finite-time cooperative control law, it can ensure that all distributed power sources can quickly and cooperatively achieve frequency recovery within a preset finite time, effectively coping with the dual interference caused by fluctuations in new energy output and network attacks. Ultimately, while saving communication resources, it significantly improves the dynamic response speed, control accuracy, and operational safety and robustness of the microgrid system.

[0080] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0081] Based on the same inventive concept, this disclosure also provides a microgrid finite-time control device with an event-triggered mechanism, corresponding to the microgrid finite-time control method with an event-triggered mechanism. Since the principle of the device in this disclosure for solving the problem is similar to the microgrid finite-time control method with an event-triggered mechanism described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0082] Reference Figure 5 The diagram shown is a schematic of a microgrid finite-time control device 500 with an event-triggered mechanism provided in an embodiment of this disclosure. The device includes: Model building module 501 is used to build a microgrid system model containing multiple distributed power sources; The error calculation module 502 is used to determine the frequency tracking error and state estimation error corresponding to each distributed power source in the microgrid system model based on the real-time frequency state data of the distributed power source. The trigger judgment module 503 is used to determine whether each distributed power source meets the event triggering conditions based on the frequency tracking error and state estimation error of each distributed power source. The control generation module 504 is used to generate a finite-time cooperative control law corresponding to each distributed power source based on the control update data received from each distributed power source when the event triggering condition is met, in response to any of the distributed power sources satisfying the event triggering condition. The control recovery module 505 is used to generate control commands for each distributed power source in the microgrid system model according to the finite-time cooperative control law, and to perform finite-time frequency recovery control on the distributed power source based on the control commands.

[0083] In some possible embodiments, the model building module 501 is specifically used for: For each distributed power source, a new energy disturbance term corresponding to the distributed power source is introduced into the dynamic equation of the microgrid system model, and the output of the new energy disturbance term corresponding to each distributed power source is set to be bounded; wherein, the new energy disturbance term is used to characterize the random power output fluctuation of new energy generation. An attack-affected term is introduced into the dynamic equations of the microgrid system model; wherein, the attack-affected term is used to characterize the disruptive state caused by denial-of-service attacks to the coordinated control process of each distributed power source; The dynamic equations of the microgrid system model are expressed as follows: ; ; in, This represents the effective control input of the i-th distributed power source at time t, which is affected by a denial-of-service attack. This is represented as the control input of the i-th distributed power source at time t; This indicates the fluctuation state of new energy sources. This indicates the existence of fluctuations in new energy sources. This indicates that there is no fluctuation in new energy sources; Let represent the new energy disturbance term of the i-th distributed power source at time t; Represented as a new energy disturbance term The output has a bounded value.

[0084] In some possible embodiments, the error calculation module 502 is specifically used for: For each distributed power source, the difference between the real-time frequency status data of the distributed power source and the reference standard frequency is calculated to obtain the frequency tracking error; For each distributed power source, the state estimation error is calculated based on the real-time frequency state data of the distributed power source and the historical frequency value at the time of the most recent event trigger.

[0085] In some possible embodiments, the trigger determination module 503 is specifically used for: In the microgrid system model, each distributed power source maintains a neighbor state cache, which is used to store the most recent control update data received from each neighboring distributed power source; wherein, the control update data includes the historical frequency values ​​of the neighboring distributed power sources of the distributed power source at the time of the most recent event triggering. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated based on its own frequency tracking error and the error between the historical frequency values ​​of each adjacent distributed power source corresponding to the distributed power source and the reference standard frequency obtained from the neighbor state cache. For each distributed power source, determine whether the absolute value of the state estimation error is not less than the product of a preset threshold and the absolute value of the asynchronous neighborhood tracking frequency error; If the determination is yes, then the distributed power source is determined to meet the event triggering condition.

[0086] In some possible embodiments, the control generation module 504 is specifically used for: When it is determined that any of the distributed power sources meets the event triggering condition, the distributed power source is used as a reference distributed power source, and the control update data is sent to each of the adjacent distributed power sources corresponding to the reference distributed power source. In the microgrid system model, after receiving the control update data sent by its neighboring distributed power sources, any distributed power source updates the neighbor state cache of that distributed power source. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated and updated based on its own frequency tracking error and the error between the historical frequency value and the reference standard frequency contained in the control update data at the most recent event trigger time of each neighboring distributed power source obtained from the updated neighbor state cache. For each distributed power source, a finite-time cooperative control law for the distributed power source is calculated and generated based on the updated asynchronous neighborhood tracking frequency error of the distributed power source and a preset feedback control function.

[0087] In some possible embodiments, the preset feedback control function is expressed as: ; ; in, This represents the control input of the i-th distributed power source at time t, i.e., the finite-time cooperative control law corresponding to the distributed power source. This represents the control gain coefficient of the first feedback term in the control law, used to adjust the strength of the first feedback term. ; This is represented as the control gain coefficient of the second feedback term in the control law, used to overcome the bounded influence of new energy disturbances. a and b represent the first and second preset coefficients, respectively, and are positive odd numbers. ; Represented as a symbolic function; It is represented as the asynchronous neighborhood tracking frequency error of the i-th distributed power source at time t when the k-th event is triggered; It is represented as the frequency measurement value of the i-th distributed power source at time t when the k-th event is triggered; This represents the frequency value of the most recent event triggering time of the i-th distributed power source, which is the neighboring distributed power source of the j-th distributed power source, at time t when the k-th event is triggered. Represented as the reference standard frequency; It is represented as the set of adjacent distributed power sources that are directly connected to the i-th distributed power source; It represents the weight coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when there is no denial-of-service attack. It is represented as the pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when there is no denial-of-service attack; This represents the state where the communication link between the i-th distributed power source and the j-th distributed power source is under attack; It represents the time-varying weighting coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when subjected to a denial-of-service attack; It is represented as the time-varying pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when subjected to a denial-of-service attack; This represents the set of time intervals in which denial-of-service attacks do not exist. This represents the set of time intervals in which a denial-of-service attack exists.

[0088] In some possible embodiments, the control generation module 504 is specifically used for: The adjacency matrix and Laplace matrix of the distributed power source are determined based on the communication topology of the microgrid system model. Monitor the impact on the communication links of the distributed power source under denial-of-service attacks; When a denial-of-service attack is detected that causes a change in the communication topology of the distributed power source, the adjacency matrix and Laplace matrix used to calculate the asynchronous neighborhood tracking frequency error are switched to matrices corresponding to the communication topology after the attack.

[0089] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 6 The diagram shows the structure of a computer device 600 provided in this embodiment of the present disclosure, including a processor 601, a memory 602, and a bus 603. The memory 602 stores execution instructions and includes a main memory 6021 and an external memory 6022. The main memory 6021, also called internal memory, is used to temporarily store computational data in the processor 601 and data exchanged with external memory 6022 such as a hard disk. The processor 601 exchanges data with the external memory 6022 through the main memory 6021.

[0090] In this embodiment, the memory 602 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 601. That is, when the computer device 600 is running, the processor 601 communicates with the memory 602 through the bus 603, so that the processor 601 executes the application code stored in the memory 602, and then executes the method described in any of the foregoing embodiments.

[0091] The memory 602 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0092] Processor 601 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0093] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 600. In other embodiments of this application, the computer device 600 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0094] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the microgrid finite-time control method with an event-triggered mechanism described in the above-described method embodiments. The storage medium can be volatile or non-volatile computer-readable storage.

[0095] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the microgrid finite-time control method with an event-triggered mechanism described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0096] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0100] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A finite-time control method for a microgrid with an event-triggered mechanism, characterized in that, include: Establish a microgrid system model that includes multiple distributed power sources; For each distributed power source in the microgrid system model, based on the real-time frequency state data of the distributed power source, the frequency tracking error and state estimation error corresponding to the distributed power source are determined. Based on the frequency tracking error and state estimation error of each distributed power source, it is determined whether each distributed power source meets the event triggering conditions. In response to any of the distributed power sources satisfying the event triggering condition, a finite-time cooperative control law corresponding to each distributed power source is generated based on the control update data received from each distributed power source when the event triggering condition is satisfied. For each distributed power source in the microgrid system model, a control command is generated according to the finite-time cooperative control law, and the distributed power source is subjected to finite-time frequency recovery control based on the control command.

2. The method according to claim 1, characterized in that, The establishment of a microgrid system model including multiple distributed power sources includes: For each distributed power source, a new energy disturbance term corresponding to the distributed power source is introduced into the dynamic equation of the microgrid system model, and the output of the new energy disturbance term corresponding to each distributed power source is set to be bounded; wherein, the new energy disturbance term is used to characterize the random power output fluctuation of new energy generation. An attack-affected term is introduced into the dynamic equations of the microgrid system model; wherein, the attack-affected term is used to characterize the disruptive state caused by denial-of-service attacks to the coordinated control process of each distributed power source; The dynamic equations of the microgrid system model are expressed as follows: ； ； in, This represents the effective control input of the i-th distributed power source at time t, which is affected by a denial-of-service attack. This is represented as the control input of the i-th distributed power source at time t; This indicates the fluctuation state of new energy sources. This indicates the existence of fluctuations in new energy sources. This indicates that there is no fluctuation in new energy sources; Let represent the new energy disturbance term of the i-th distributed power source at time t; Represented as a new energy disturbance term The output has a bounded value.

3. The method according to claim 1, characterized in that, The determination of the frequency tracking error and state estimation error corresponding to the distributed power source includes: For each distributed power source, the difference between the real-time frequency status data of the distributed power source and the reference standard frequency is calculated to obtain the frequency tracking error; For each distributed power source, the state estimation error is calculated based on the real-time frequency state data of the distributed power source and the historical frequency value at the time of the most recent event trigger.

4. The method according to claim 3, characterized in that, The determination of whether each distributed power source meets the event triggering conditions based on the frequency tracking error and state estimation error of each distributed power source includes: In the microgrid system model, each distributed power source maintains a neighbor state cache, which is used to store the most recent control update data received from each neighboring distributed power source; wherein, the control update data includes the historical frequency values ​​of the neighboring distributed power sources of the distributed power source at the time of the most recent event triggering. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated based on its own frequency tracking error and the error between the historical frequency values ​​of each adjacent distributed power source corresponding to the distributed power source and the reference standard frequency obtained from the neighbor state cache. For each distributed power source, determine whether the absolute value of the state estimation error is not less than the product of a preset threshold and the absolute value of the asynchronous neighborhood tracking frequency error; If the determination is yes, then the distributed power source is determined to meet the event triggering condition.

5. The method according to claim 4, characterized in that, The step of generating a finite-time cooperative control law corresponding to each distributed power source based on control update data received from each distributed power source when the event triggering condition is met includes: When it is determined that any of the distributed power sources meets the event triggering condition, the distributed power source is used as a reference distributed power source, and the control update data is sent to each of the adjacent distributed power sources corresponding to the reference distributed power source. In the microgrid system model, after receiving the control update data sent by its neighboring distributed power sources, any distributed power source updates the neighbor state cache of that distributed power source. For each distributed power source, the asynchronous neighborhood tracking frequency error of the distributed power source is calculated and updated based on its own frequency tracking error and the error between the historical frequency value and the reference standard frequency contained in the control update data at the most recent event trigger time of each neighboring distributed power source obtained from the updated neighbor state cache. For each distributed power source, a finite-time cooperative control law for the distributed power source is calculated and generated based on the updated asynchronous neighborhood tracking frequency error of the distributed power source and a preset feedback control function.

6. The method according to claim 5, characterized in that, The preset feedback control function is expressed as follows: ； ； in, This represents the control input of the i-th distributed power source at time t, i.e., the finite-time cooperative control law corresponding to the distributed power source. This represents the control gain coefficient of the first feedback term in the control law, used to adjust the strength of the first feedback term. ; This is represented as the control gain coefficient of the second feedback term in the control law, used to overcome the bounded influence of new energy disturbances. a and b represent the first and second preset coefficients, respectively, and are positive odd numbers. ; Represented as a symbolic function; It is represented as the asynchronous neighborhood tracking frequency error of the i-th distributed power source at time t when the k-th event is triggered; It is represented as the frequency measurement value of the i-th distributed power source at time t when the k-th event is triggered; This represents the frequency value of the most recent event triggering time of the i-th distributed power source, which is the neighboring distributed power source of the j-th distributed power source, at time t when the k-th event is triggered. Indicated as reference standard frequency; It is represented as the set of adjacent distributed power sources that are directly connected to the i-th distributed power source; It represents the weight coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when there is no denial-of-service attack. It is represented as the pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when there is no denial-of-service attack; This represents the state where the communication link between the i-th distributed power source and the j-th distributed power source is under attack; It represents the time-varying weighting coefficient of the communication link between the i-th distributed power source and the j-th distributed power source when subjected to a denial-of-service attack; It is represented as the time-varying pinning gain coefficient between the i-th distributed power source and the reference standard frequency node when subjected to a denial-of-service attack; This represents the set of time intervals in which denial-of-service attacks do not exist. This represents the set of time intervals in which a denial-of-service attack exists.

7. The method according to claim 5, characterized in that, The calculation and updating of the asynchronous neighborhood tracking frequency error of the distributed power source includes: The adjacency matrix and Laplace matrix of the distributed power source are determined based on the communication topology of the microgrid system model. Monitor the impact on the communication links of the distributed power source under denial-of-service attacks; When a denial-of-service attack is detected that causes a change in the communication topology of the distributed power source, the adjacency matrix and Laplace matrix used to calculate the asynchronous neighborhood tracking frequency error are switched to matrices corresponding to the communication topology after the attack.

8. A microgrid finite-time control device with an event-triggered mechanism, characterized in that, include: The model building module is used to build a microgrid system model that includes multiple distributed power sources; The error calculation module is used to determine the frequency tracking error and state estimation error corresponding to each distributed power source in the microgrid system model based on the real-time frequency state data of the distributed power source. The triggering judgment module is used to determine whether each distributed power source meets the event triggering conditions based on the frequency tracking error and state estimation error of each distributed power source. A control generation module is configured to, in response to any of the distributed power sources satisfying the event triggering condition, generate a finite-time cooperative control law corresponding to each distributed power source based on control update data received from each distributed power source when the event triggering condition is satisfied. The control recovery module is used to generate control commands for each distributed power source in the microgrid system model according to the finite-time cooperative control law, and to perform finite-time frequency recovery control on the distributed power source based on the control commands.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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