A collaborative control method and system for DC microgrids

Abstract

This application provides a collaborative control method and system for a DC microgrid. The method is applied to a collaborative control system for a DC microgrid including an energy management unit and a power conversion unit. The power conversion unit includes an edge intelligent control module and a low-level hard logic control module. The method acquires fault analysis data of the target DC microgrid and establishes a local expert database. The energy management unit and the power conversion unit communicate hierarchically, enabling the edge intelligent control module to receive macroscopic combination commands and determine a target power reference value based on the macroscopic combination commands and the local expert database. In response to the detection of a voltage change in the target DC microgrid, the method adjusts the power output value of the target DC microgrid based on the target power reference value. In response to the detection of a fault in the target DC microgrid, the method performs fault handling according to the fault level. Through this method, the efficient, stable, intelligent, and safe operation of the DC microgrid is achieved, and it is adaptable to the operational requirements under various complex working conditions.

Description

Technical Field

[0001] This application relates to the field of DC microgrid control technology, and in particular to a collaborative control method and system for DC microgrids. Background Technology

[0002] In existing DC microgrid control systems, a control architecture of "centralized decision-making by the energy management system and passive execution by the power conversion system" is commonly adopted. Under this architecture, the underlying devices (such as power conversion systems, photovoltaics, energy storage, charging piles, diesel generators, etc.) collect voltage, current and power data in real time and upload them to the energy management system. The energy management system is responsible for fault diagnosis, energy management algorithm calculation and power allocation command generation, and sends the commands to each device. The power conversion system and other devices only act as passive execution units, mechanically executing commands to maintain the stability of the DC bus voltage, and lack autonomous decision-making capabilities.

[0003] However, existing DC microgrid control systems have significant drawbacks: they do not fully integrate the physical characteristics of DC microgrids, resulting in insufficient core capabilities and a lack of intelligence in the power conversion system; the interaction between the power conversion system and the energy management system is simplistic and cannot handle fuzzy, multi-state, or inter-regional scheduling commands; the fault handling mechanism is slow to respond and lacks hierarchical autonomy; in addition, the control system has a complex architecture, high computing power requirements, and is prone to conflicts, making it difficult to ensure system stability. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a collaborative control method and system for DC microgrids. By introducing core technologies such as a hub-type control topology with a local expert database, an edge intelligent control module on the power conversion side, a knowledge graph corpus, an intelligent interactive command system, and a hierarchical fault self-governance mechanism, the core control role of the power conversion unit is fully utilized. This enables the DC microgrid to overcome the problems of passive execution of the power conversion unit, insufficient intelligence, simple fault handling mechanism, slow response speed, poor system stability, and complex architecture in existing DC microgrid control systems in terms of stability, real-time performance, intelligence, adaptability, and security. It achieves efficient, stable, intelligent, and safe operation of the DC microgrid and adapts to the operational needs under various complex working conditions.

[0005] This application provides a collaborative control method for a DC microgrid. The collaborative control method is applied to a collaborative control system for the DC microgrid, which includes an energy management unit and a power conversion unit. The power conversion unit includes an edge intelligent control module and a low-level hard logic control module. The collaborative control method includes: In response to the power-on of the collaborative control system, the energy management unit acquires the fault analysis data corresponding to the target DC microgrid and sends the fault analysis data to the edge intelligent control module, which then establishes a local expert database based on the fault analysis data. The energy management unit and the power conversion unit communicate in a hierarchical manner so that the edge intelligent control module receives the macroscopic combination command sent by the energy management unit, and the edge intelligent control module determines the target power reference value corresponding to the target DC microgrid based on the macroscopic combination command and the local expert database. In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module adjusts the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid; In response to the power conversion unit detecting a fault in the target DC microgrid, the edge intelligent control module or the underlying hard logic control module performs corresponding fault handling based on the fault level corresponding to the fault.

[0006] Furthermore, in response to the power-on of the collaborative control system, the energy management unit acquires fault analysis data corresponding to the target DC microgrid and sends the fault analysis data to the edge intelligent control module. The edge intelligent control module then establishes a local expert database based on the fault analysis data, including: In response to the power-on of the collaborative control system, the energy management unit obtains fault analysis data corresponding to the target DC microgrid from the cloud platform and sends the fault analysis data to the edge intelligent control module through the communication channel; wherein, the fault analysis data includes at least fault list information and fault knowledge graph information; The edge intelligent control module receives the fault analysis data and verifies and classifies the fault analysis data. The fault analysis data after verification and classification is loaded into the local storage medium to establish a local expert database.

[0007] Furthermore, the energy management unit and the power conversion unit communicate in a hierarchical manner, enabling the edge intelligent control module to receive macroscopic combined commands sent by the energy management unit, including: The underlying hard logic control module collects telemetry data corresponding to the target DC microgrid and sends the telemetry data to the energy management unit through a preset hard channel; The energy management unit receives the telemetry data, generates a macroscopic combination command based on the telemetry data, and sends the macroscopic combination command to the edge intelligent control module through a preset soft channel; The macroscopic combined instruction is received by the edge intelligent control module.

[0008] Furthermore, the step of determining the target power reference value corresponding to the target DC microgrid by the edge intelligent control module based on the macroscopic combined instructions and the local expert database includes: In response to receiving the macroscopic combined instruction, the edge intelligent control module acquires telemetry data in real time in the underlying hard logic control module; Based on the telemetry data, the macroscopic combined instructions are semantically parsed using the local expert database to determine the target power reference value of the target DC microgrid at the current moment, and the target power reference value is synchronized to the underlying hard logic control module.

[0009] Furthermore, in response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module adjusts the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid, including: In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module obtains the control topology knowledge information corresponding to the voltage change from the local expert database. Based on the control topology information, the duty cycle of the bidirectional current converter in the target DC microgrid is adjusted according to the target power reference value to adjust the power output value of the target DC microgrid and clamp the bus voltage of the target DC microgrid.

[0010] Furthermore, when the fault level is a first fault level or a second fault level, the fault handling performed by the edge intelligent control module or the underlying hard logic control module includes: Based on the fault information corresponding to the fault, the edge intelligent control module retrieves the response mode information from the local expert database. The fault is handled accordingly based on the response mode information, and the target DC microgrid is controlled to enter the derating self-healing mode. The fault handling chain information is sent to the energy management unit so that the energy management unit can update the cloud knowledge base in the cloud platform and synchronize it to each device in the target DC microgrid based on the fault handling chain information.

[0011] Furthermore, when the fault level is the third fault level, the fault handling performed by the edge intelligent control module or the underlying hard logic control module includes: The underlying hard logic control module blocks the pulse width modulation signal corresponding to the target DC microgrid and disconnects the main contactor in the target DC microgrid to perform corresponding fault handling.

[0012] This application embodiment also provides a collaborative control system for a DC microgrid, the collaborative control system including an energy management unit and a power conversion unit; wherein, the power conversion unit includes an edge intelligent control module and a low-level hard logic control module; The energy management unit is used to respond to the power-on of the collaborative control system, acquire fault analysis data corresponding to the target DC microgrid, and send the fault analysis data to the edge intelligent control module. The edge intelligent control module is used to establish a local expert database based on the fault analysis data; receive macroscopic combination commands sent by the energy management unit through hierarchical communication between the energy management unit and the power conversion unit, and determine the target power reference value corresponding to the target DC microgrid based on the macroscopic combination commands and the local expert database; in response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, adjust the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid; and in response to the power conversion unit detecting a fault in the target DC microgrid, perform corresponding fault handling according to the fault level corresponding to the fault. The underlying hard logic control module is used to detect voltage changes in the target DC microgrid; in response to the power conversion unit detecting a fault in the target DC microgrid, it performs corresponding fault handling according to the fault level corresponding to the fault.

[0013] This application also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the cooperative control method for DC microgrids described above are performed.

[0014] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described coordinated control method for a DC microgrid.

[0015] The present application provides a collaborative control method and system for DC microgrids. The collaborative control method is applied to a collaborative control system for a DC microgrid, which includes an energy management unit and a power conversion unit. The power conversion unit includes an edge intelligent control module and a low-level hard logic control module. The collaborative control method includes: in response to power-on of the collaborative control system, the energy management unit acquires fault analysis data corresponding to the target DC microgrid and sends the fault analysis data to the edge intelligent control module; the edge intelligent control module then establishes a local expert database based on the fault analysis data. The energy management unit and the power conversion unit communicate through hierarchical communication to enable the edge intelligent control module to... The control module receives a macroscopic combination command sent by the energy management unit, and the edge intelligent control module determines the target power reference value corresponding to the target DC microgrid based on the macroscopic combination command and the local expert database. In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module adjusts the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid. In response to the power conversion unit detecting a fault in the target DC microgrid, the edge intelligent control module or the underlying hard logic control module performs corresponding fault handling according to the fault level corresponding to the fault.

[0016] Compared with the existing control architecture that adopts "centralized decision-making by the energy management system and passive execution by the power conversion system", this method fully leverages the core control role of the power conversion unit by introducing core technologies such as a hub-type control topology with a local expert database, edge intelligent control modules on the power conversion side, a knowledge graph corpus, an intelligent interactive command system, and a hierarchical fault self-governance mechanism. This enables the DC microgrid to overcome the problems of passive execution by the power conversion unit, insufficient intelligence, simple fault handling mechanism, slow response speed, poor system stability, and complex architecture in the existing DC microgrid control system in terms of stability, real-time performance, intelligence, adaptability, and security. It achieves efficient, stable, intelligent, and safe operation of the DC microgrid and adapts to the operational needs of various complex working conditions.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a collaborative control method for a DC microgrid provided in an embodiment of this application; Figure 2 A schematic diagram of the structure of a collaborative control system for a DC microgrid provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application 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 application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0021] Research has revealed that existing DC microgrid control systems commonly employ a control architecture of "centralized decision-making by the energy management system and passive execution by the power conversion system." Under this architecture, data acquisition involves the following steps: Lower-level devices (such as power conversion systems, photovoltaic systems, energy storage systems, charging piles, and diesel generators) collect their own voltage, current, and power data in real time and upload them to the upper-level energy management system via communication links. The energy management system diagnoses faults in the collected remote signaling points and takes corresponding actions based on the fault level. As the sole "brain," the energy management system executes energy management algorithms to calculate power allocation commands or parameter settings for each device. The energy management system then issues the calculation results as scheduling commands to the power conversion system and other devices. The power conversion system and other devices, acting as purely "executors," lack autonomous thinking capabilities and mechanically execute the power or parameter adjustment commands issued by the energy management system to maintain the stability of the DC bus voltage and the operation of the system.

[0022] However, existing DC microgrid control systems have significant drawbacks: the current DC microgrid control architecture does not fully reflect the physical characteristics of DC microgrids, and the core control power conversion system lacks sufficient capabilities; the power conversion system, as the core control node of the system, is always subject to passive control by the energy management system, lacking intelligent and autonomous decision-making capabilities; the power conversion system lacks a reusable knowledge graph and system, and usually only interacts with the energy management system through a fixed communication protocol, making it impossible for equipment to make decisions or self-heal based on historical experience and brand characteristics; the scheduling interaction mode between the energy management system and the power conversion system is simplistic and cannot handle fuzzy, multi-state, and interval scheduling commands; the current microgrid system fault handling mechanism is simplistic, with insufficient response speed, lack of hierarchical autonomy, and is complex in structure, requires high computing power, and is prone to conflicts, making it difficult to ensure system stability.

[0023] Based on this, this application provides a collaborative control method for DC microgrids. By introducing core technologies such as a hub-type control topology with a local expert database, an edge intelligent control module on the power conversion side, a knowledge graph corpus, an intelligent interactive command system, and a hierarchical fault self-governance mechanism, the method fully leverages the core control role of the power conversion unit. This enables the DC microgrid to overcome the problems of passive execution of the power conversion unit, insufficient intelligence, simple fault handling mechanism, slow response speed, poor system stability, and complex architecture in existing DC microgrid control systems in terms of stability, real-time performance, intelligence, adaptability, and security. The method achieves efficient, stable, intelligent, and safe operation of the DC microgrid and adapts to the operational needs under various complex working conditions.

[0024] Please see Figure 1 , Figure 1 This is a flowchart illustrating a collaborative control method for a DC microgrid provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the collaborative control method for DC microgrids is typically applied in the collaborative control system of DC microgrids.

[0025] In this embodiment of the application, the collaborative control system includes an energy management unit and a power conversion unit; wherein, the power conversion unit includes an edge intelligent control module and a low-level hard logic control module.

[0026] Here, the collaborative control system is a specially constructed integrated control system for DC microgrids, including an energy management unit and a power conversion unit. The collaborative control system also has a corresponding cloud platform, which serves as the global data and knowledge aggregation layer. The energy management unit is the core control node of the station control layer, and the power conversion unit is the core execution node of the edge layer. The three work together efficiently through a high-speed communication link to ensure smooth data interaction and timely command transmission.

[0027] The energy management unit is located at the station control layer of the DC microgrid and runs on dedicated industrial control equipment. It is mainly used to formulate the system's global macro control strategy, acquire and issue fault analysis data, realize the visual monitoring of various local operating data of the station, receive operating data and fault handling information reported by the power conversion unit and issue macro combined instructions. It is the core carrier of the system's macro decision-making.

[0028] The power conversion unit is the core execution device of the entire collaborative control system. It is located at the edge layer of the DC microgrid and directly connects to the DC bus, photovoltaic modules, energy storage batteries and other equipment. Its internal controller is logically divided into two parts: the edge intelligent control module and the bottom hard logic control module. It is mainly responsible for core functions such as energy power conversion, DC bus voltage regulation, operation fault detection and hierarchical processing.

[0029] Among them, the edge intelligent control module runs on a high-performance embedded processor (such as the ARM Cortex-A9 series) and has core functions such as semantic parsing, autonomous voltage regulation and power adjustment, knowledge base retrieval, and fault self-healing decision-making. It is the core module for realizing the autonomous decision-making and intelligent operation of the power conversion unit and can independently complete various routine control and minor fault handling without the energy management unit. The underlying hard logic control module runs on a DSP (Digital Signal Processor) or FPGA (Field Programmable Gate Array) and has core functions such as high-frequency pulse width modulation, fast analog signal acquisition, and three-level fault hard cutoff. It is responsible for quickly executing low-level control commands and handling emergency faults to ensure the timeliness and safety of fault response.

[0030] like Figure 1 As shown, the cooperative control method includes: S101. In response to the power-on of the collaborative control system, the energy management unit obtains the fault analysis data corresponding to the target DC microgrid and sends the fault analysis data to the edge intelligent control module. The edge intelligent control module then establishes a local expert database based on the fault analysis data.

[0031] In this embodiment of the application, the fault analysis data is a data set used to support the edge intelligent control module in realizing autonomous decision-making and fault handling, including but not limited to fault list information and fault knowledge graph information.

[0032] The fault list information mainly includes the operating warning thresholds of various equipment (such as the voltage warning threshold of a certain type of battery in a low-temperature environment), while the fault knowledge graph information mainly includes the correspondence between specific operating waveforms and fault modes, standardized handling strategies for various faults, and knowledge related to system control topology.

[0033] In this embodiment, the local expert database is established by the edge intelligent control module based on the received fault analysis data and stored in the local high-speed storage medium (such as Flash memory chip) of the power conversion unit. It includes, but is not limited to, core contents such as fault handling experience, equipment operating parameters, control strategies, and fault mode matching rules, which are used to support the semantic parsing, rapid fault retrieval and autonomous decision-making of the edge intelligent control module to realize local intelligent control.

[0034] In one possible implementation of this application, step S101 may include: S1011. In response to the power-on of the collaborative control system, the energy management unit obtains the fault analysis data corresponding to the target DC microgrid from the cloud platform and sends the fault analysis data to the edge intelligent control module through the communication channel.

[0035] The fault analysis data includes at least fault list information and fault knowledge graph information.

[0036] In this step, in response to the power-on startup of the collaborative control system, the energy management unit obtains the fault analysis data corresponding to the target DC microgrid from the cloud platform through a preset communication link.

[0037] Among them, the cloud platform, as the global data aggregation layer corresponding to the collaborative control system, is mainly used to realize core functions such as real-time global data monitoring of the site, operational benefit analysis, centralized aggregation of knowledge graphs, aggregation of problem lists, and multi-site group adjustment and control. It stores fault analysis data of the entire site and updates and optimizes it regularly to ensure the timeliness and completeness of the data.

[0038] Furthermore, the energy management unit accurately transmits the acquired fault analysis data to the edge intelligent control module of each power conversion unit through a preset communication channel. A data verification mechanism is used during the transmission process to ensure that no data loss or mistransmission occurs during data transmission.

[0039] S1012. The edge intelligent control module receives the fault analysis data and verifies and classifies the fault analysis data.

[0040] In this step, after receiving the fault analysis data, the edge intelligent control module first performs strict verification and classification on the data. It uses a preset verification algorithm to remove invalid data and correct abnormal data, ensuring the accuracy and completeness of the fault analysis data and laying the foundation for the establishment of a local expert database.

[0041] S1013. Load the fault analysis data after verification and classification into the local storage medium to establish a local expert database.

[0042] In this step, the edge intelligent control module loads the fault analysis data after verification and classification into the local storage medium, and establishes a local expert library containing fault handling experience, equipment operating parameters, control strategies, fault mode matching rules, etc., so that the power conversion unit has complete adjustment experience and fault recovery experience at the beginning of startup, providing solid knowledge support for subsequent autonomous decision-making, power regulation and fault handling.

[0043] S102. The energy management unit and the power conversion unit communicate in a hierarchical manner so that the edge intelligent control module receives the macroscopic combination command sent by the energy management unit, and the edge intelligent control module determines the target power reference value corresponding to the target DC microgrid based on the macroscopic combination command and the local expert library.

[0044] In this embodiment, hierarchical communication is an efficient communication mode between the energy management unit and the power conversion unit, specifically including two independent communication links: a hard channel and a soft channel.

[0045] The hard channel is mainly used for the rapid reporting of telemetry data between the underlying hard logic control module and the energy management unit, ensuring the real-time transmission of data; the soft channel is mainly used for the macroscopic combined command interaction between the energy management unit and the edge intelligent control module, realizing the accuracy of command transmission, and greatly improving communication efficiency and reliability through classified transmission.

[0046] In this embodiment, the macro-combination instruction is a comprehensive scheduling instruction generated by the energy management unit based on the system's global operating status, load demand, and renewable energy output.

[0047] The macroscopic combined instructions include, but are not limited to, fuzzy, multi-state, or interval scheduling requirements (e.g., "Execute gradient power, target interval [50kW, 100kW], priority: frequency control"), which are different from the single fixed instructions in the prior art and can meet the diverse scheduling needs under complex operating conditions.

[0048] In this embodiment, the target power reference value is the optimal power output parameter calculated by the edge intelligent control module based on macroscopic combined instructions and local expert library, combined with real-time remote sensing data of DC microgrid (i.e., real-time operating status, such as battery SOC, bus voltage, and load changes). It is used to accurately guide the underlying hard logic control module to perform power regulation operations, ensuring the economy and stability of system operation.

[0049] In one possible implementation of this application, in specific implementation, the step S102, in which the energy management unit and the power conversion unit communicate in a hierarchical manner so that the edge intelligent control module receives the macroscopic combined command sent by the energy management unit, may include: S1021. The underlying hard logic control module collects the telemetry data corresponding to the target DC microgrid and sends the telemetry data to the energy management unit through a preset hard channel.

[0050] In this step, the underlying hard logic control module of the power conversion unit collects telemetry data corresponding to the target DC microgrid in real time through its own onboard sensors.

[0051] The telemetry data includes, but is not limited to, real-time operating data such as the bus voltage and current of the DC microgrid, the temperature of various equipment, and the battery SOC (State of Charge).

[0052] Furthermore, the underlying hard logic control module rapidly sends the telemetry data to the energy management unit via a preset hard channel at millisecond intervals, ensuring that the system's operating status can be monitored and accurately grasped in real time.

[0053] S1022. The energy management unit receives the telemetry data, generates a macroscopic combination command based on the telemetry data, and sends the macroscopic combination command to the edge intelligent control module through a preset soft channel.

[0054] In this step, after receiving the telemetry data reported by the underlying hard logic control module, the energy management unit formulates a macro control strategy based on the global system operation requirements (such as maximizing operating benefits, load balancing, and renewable energy consumption), generates corresponding macro combination instructions, and accurately sends the macro combination instructions to the edge intelligent control module through a preset soft channel to ensure the timeliness and accuracy of instruction transmission.

[0055] S1023, The edge intelligent control module receives the macroscopic combination command.

[0056] In one possible implementation of this application, in specific implementation, the step S102 in which the edge intelligent control module determines the target power reference value corresponding to the target DC microgrid based on the macroscopic combined instructions and the local expert database may include: S1024. In response to receiving the macroscopic combination command, the edge intelligent control module acquires telemetry data in real time in the underlying hard logic control module.

[0057] S1025. Based on the telemetry data, the macroscopic combined command is semantically parsed using the local expert database to determine the target power reference value corresponding to the target DC microgrid at the current moment, and the target power reference value is synchronized to the underlying hard logic control module.

[0058] In this embodiment, after receiving the macro-combined command, the edge intelligent control module no longer waits for the energy management unit's second-by-second scheduling command. Instead, it actively acquires the latest telemetry data in real time from the underlying hard logic control module. Combining fault handling experience and control topology knowledge from the local expert database, it performs precise semantic parsing of the macro-combined command, clarifies the core requirements such as power target and control priority in the command, autonomously calculates the optimal target power reference value at the current moment, and synchronizes the target power reference value to the underlying hard logic control module, providing a precise basis for subsequent power adjustment operations.

[0059] S103. In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module adjusts the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid.

[0060] In this embodiment of the application, when the photovoltaic output of the target DC microgrid suddenly fluctuates, it will cause the bus voltage transmission to deviate, and the voltage change transmitted by the target DC microgrid can be detected by the underlying hard logic.

[0061] In one possible implementation of this application, step S103 may include: S1031. In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module obtains the control topology knowledge information corresponding to the voltage change from the local expert database.

[0062] Here, the power conversion unit serves as the voltage anchor point of the DC bus in the target DC microgrid, directly affecting the stability of the bus voltage.

[0063] In this step, the underlying hard logic control module continuously detects the bus voltage of the DC microgrid through a high-precision voltage sensor, captures voltage changes in real time (such as sudden fluctuations in photovoltaic output or bus voltage deviations from the preset range caused by sudden load changes), and quickly feeds back voltage change signals.

[0064] Furthermore, when the underlying hard logic control module detects a change in the bus voltage that exceeds the preset fluctuation range, it immediately feeds back the voltage anomaly signal to the edge intelligent control module. The edge intelligent control module quickly retrieves the control topology knowledge information corresponding to the voltage change from the local expert database and accurately determines the power regulation strategy adapted to the current operating condition.

[0065] S1032. Based on the control topology knowledge information, adjust the duty cycle of the bidirectional current converter in the target DC microgrid according to the target power reference value, so as to adjust the power output value of the target DC microgrid and clamp the bus voltage of the target DC microgrid.

[0066] In this step, the edge intelligent control module, based on the retrieved control topology knowledge information, quickly adjusts the duty cycle of the bidirectional current converter (bidirectional DC / AC or DC / DC converter) in the DC microgrid according to the target power reference value. Through precise adjustment of the power output value, it effectively clamps the DC bus voltage, ensuring that the bus voltage quickly returns to the preset stable range.

[0067] Here, the entire voltage regulation and power adjustment process does not require the intervention of the energy management unit. It is completed by the edge intelligent control module and the underlying hard logic control module in collaboration, achieving millisecond-level edge-side closed-loop control. This enables rapid response to sudden operating conditions such as photovoltaic output fluctuations and load changes, ensuring that the DC bus voltage remains stable at all times.

[0068] S104. In response to the power conversion unit detecting a fault in the target DC microgrid, the edge intelligent control module or the underlying hard logic control module performs corresponding fault handling according to the fault level corresponding to the fault.

[0069] In the embodiments of this application, the fault categories are divided based on the severity of the fault, the scope of the harm, and the handling method. The fault levels include a first fault level, a second fault level, and a third fault level.

[0070] Among them, the first and second fault levels are collectively referred to as level one and two faults, which are minor faults that can be self-healed through flexible handling without downtime; the third fault level is a serious fault that requires a rigid blocking method to quickly cut off the fault source and prevent the fault from escalating.

[0071] In one possible implementation of this application, when the fault level is a first fault level or a second fault level, the step S104 in which the edge intelligent control module or the underlying hard logic control module performs corresponding fault handling may include: S1041. The edge intelligent control module retrieves the response mode information from the local expert database based on the fault information corresponding to the fault.

[0072] In this step, the edge intelligent control module or the underlying hard logic control module of the power conversion unit detects system faults in real time through its own sensors and detection circuits and synchronously collects the corresponding fault information (e.g., abnormal temperature values, abnormal operating waveforms, fault occurrence time, etc.) to provide a basis for fault handling.

[0073] Furthermore, based on the collected fault information, the edge intelligent control module quickly searches and matches in the local expert database to find the fault response mode information corresponding to the current fault (such as the handling strategy corresponding to the fault mode "fan dust accumulation leads to reduced heat dissipation efficiency"), ensuring that the fault handling is targeted.

[0074] S1042. Perform corresponding fault handling according to the response mode information, and control the target DC microgrid to enter the derating self-healing mode.

[0075] In this step, the edge intelligent control module handles the fault flexibly according to the retrieved response mode information. Instead of triggering the device's charge / discharge restriction operation, it achieves autonomous self-healing of the fault by adjusting the device's operating parameters (such as automatically increasing the fan speed and optimizing heat dissipation efficiency) and limiting the charging and discharging power (such as limiting it to 50%). At the same time, it controls the target DC microgrid to enter the derating self-healing mode to ensure that the system can continue to operate stably and reduce downtime losses.

[0076] S1043. The fault handling chain information is sent to the energy management unit so that the energy management unit can update the cloud knowledge base in the cloud platform and synchronize it to each device in the target DC microgrid based on the fault handling chain information.

[0077] In this step, after the fault handling is completed, the edge intelligent control module sends the complete fault handling chain information to the energy management unit. After receiving the handling chain information, the energy management unit updates it as a new problem list item to the cloud knowledge base in the cloud platform and supplements and improves the fault knowledge graph to achieve knowledge accumulation.

[0078] The information in the handling chain includes, but is not limited to, fault phenomena, handling measures, and handling results, such as abnormal temperature, fan speed increase, and return to normal.

[0079] Furthermore, the energy management unit periodically synchronizes the updated cloud knowledge base and fault knowledge graph in the cloud platform to each power conversion unit in the target DC microgrid, enabling the fault handling experience of individual power conversion units to evolve into intelligent whole-station systems, significantly improving the fault handling capability and intelligence of the entire system.

[0080] In one possible implementation of this application, when the fault level is the third fault level, the step S104 in which the edge intelligent control module or the underlying hard logic control module performs corresponding fault handling may include: S1044. The underlying hard logic control module blocks the pulse width modulation signal corresponding to the target DC microgrid and disconnects the main contactor in the target DC microgrid to perform corresponding fault handling for the fault.

[0081] In this embodiment, the underlying hard logic control module detects the third fault level (serious fault, such as DC bus short circuit) in real time through a high-precision detection circuit. The third fault level has a high degree of hazard and spreads rapidly. The fault source must be cut off immediately to prevent the fault from spreading and causing equipment damage or safety accidents.

[0082] In this step, the underlying hard logic control module directly performs a rigid blocking operation within microseconds. Specifically, this includes quickly blocking the pulse width modulation (PWM) signal corresponding to the target DC microgrid and immediately disconnecting the main contactor in the DC microgrid to quickly cut off the fault circuit and prevent the fault from escalating further.

[0083] Here, the fault handling process for the third fault level does not go through the edge intelligent control module, but is directly executed independently by the underlying hard logic control module. This ensures that even in extreme situations such as software crashes or communication interruptions in the edge intelligent control module, it can still respond quickly and handle the situation effectively, maximizing the safety of system equipment and on-site personnel.

[0084] Thus, the collaborative control method described in this application, compared with the prior art, can effectively solve various defects and deficiencies of the prior art through innovative technical solutions and control logic, and significantly improve the operating performance of the target DC microgrid system.

[0085] Specifically, this application proposes a hub-type control topology adapted to the physical characteristics of the target DC microgrid. It uses the power conversion unit as the core control node to fully leverage the core control role of the power conversion unit. At the same time, it abandons the multi-agent collaborative architecture, effectively avoiding decision-making conflicts and operational oscillations between agents. It combines edge-side closed-loop control of the edge intelligent control module and the underlying hard logic control module to quickly respond to operating conditions such as photovoltaic power output fluctuations and load changes, ensuring the stability of the bus voltage of the target DC microgrid and significantly improving the operational stability and power supply reliability of the target DC microgrid system.

[0086] Furthermore, a layered communication mode is adopted, with the bottom-level hard logic control module reporting telemetry data at millisecond intervals to ensure that the target DC microgrid's operating status is controllable in real time; the edge intelligent control module achieves millisecond-level bus voltage regulation response to quickly respond to changes in operating conditions; and the bottom-level hard logic control module achieves microsecond-level third-level fault blocking to quickly cut off the fault source. The entire control and fault handling process does not rely on centralized scheduling by the energy management unit, which greatly shortens the response time and significantly improves the real-time performance of the target DC microgrid system.

[0087] Furthermore, an edge intelligent control module is embedded in the power conversion unit to establish a comprehensive local expert database. This enables semantic parsing of macroscopic combined commands, autonomous decision-making, and self-healing of first and second fault levels, transforming the power conversion unit from traditional "passive execution" to "intelligent autonomy." Simultaneously, through a self-evolutionary closed-loop mechanism, fault handling chain information is reported to the energy management unit, updating the cloud knowledge base in the cloud platform and synchronizing it to each power conversion unit. This allows the fault handling experience of individual power conversion units to evolve into intelligent systems across the entire system, continuously enriching the system's knowledge base and significantly improving the intelligence and adaptability of the target DC microgrid system.

[0088] Furthermore, a scientific and reasonable hierarchical fault self-governance mechanism is adopted. First and second level faults are handled through flexible self-healing, and fault recovery can be achieved without downtime, effectively avoiding downtime losses of the target DC microgrid system. Third level faults are quickly cut off from the fault source through the rigid blocking method of the underlying hard logic control module, ensuring the safety of system equipment and on-site personnel even in extreme cases. This improves the fault handling system and greatly enhances the operational safety of the target DC microgrid system.

[0089] Furthermore, a multi-state command intelligent interaction mode for the energy management unit and the power conversion unit is proposed. The edge intelligent control module can accurately analyze the fuzzy, interval or combined scheduling requirements in the macro-combined commands, and can flexibly adapt to the scheduling needs of various working conditions of photovoltaic, energy storage and diesel. Unlike the single fixed communication scheduling mode in the existing technology, the communication flexibility and working condition adaptability of the target DC microgrid system are greatly improved.

[0090] Furthermore, the energy management unit uniformly formulates macro-control strategies, and the power conversion unit, as the core node, achieves edge intelligent autonomy through its embedded edge intelligent control module. This effectively reduces the computing power requirements of the target DC microgrid system, simplifies the system architecture, reduces equipment investment and maintenance costs, and significantly improves system operating efficiency and reliability, making it highly practical.

[0091] The collaborative control method for DC microgrids provided in this application, through the introduction of core technologies such as a hub-type control topology with a local expert database, an edge intelligent control module on the power conversion side, a knowledge graph corpus, an intelligent interactive command system, and a hierarchical fault self-governance mechanism, fully leverages the core control role of the power conversion unit. This enables the DC microgrid to overcome the problems of passive execution of the power conversion unit, insufficient intelligence, simple fault handling mechanism, slow response speed, poor system stability, and complex architecture in existing DC microgrid control systems in terms of stability, real-time performance, intelligence, adaptability, and security. It achieves efficient, stable, intelligent, and safe operation of the DC microgrid and adapts to the operational needs under various complex working conditions.

[0092] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a collaborative control system for a DC microgrid provided in an embodiment of this application. Figure 2 As shown, the collaborative control system 200 includes an energy management unit 210 and a power conversion unit 220; wherein, the power conversion unit 220 includes an edge intelligent control module 221 and a low-level hard logic control module 222. The energy management unit 210 is used to respond to the power-on of the collaborative control system, acquire fault analysis data corresponding to the target DC microgrid, and send the fault analysis data to the edge intelligent control module. The edge intelligent control module 221 is used to establish a local expert database based on the fault analysis data; receive macroscopic combination instructions sent by the energy management unit through hierarchical communication between the energy management unit and the power conversion unit, and determine the target power reference value corresponding to the target DC microgrid based on the macroscopic combination instructions and the local expert database; in response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, adjust the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid; in response to the power conversion unit detecting a fault in the target DC microgrid, perform corresponding fault handling according to the fault level corresponding to the fault. The underlying hard logic control module 222 is used to detect voltage changes in the target DC microgrid; in response to the power conversion unit detecting a fault in the target DC microgrid, it performs corresponding fault handling according to the fault level corresponding to the fault.

[0093] The collaborative control system for DC microgrids provided in this application, through the introduction of a hub-type control topology with a local expert database, an edge intelligent control module on the power conversion side, a knowledge graph corpus, an intelligent interactive command system, and a hierarchical fault self-governance mechanism, fully leverages the core control role of the power conversion unit. This enables the DC microgrid to overcome the problems of passive execution of the power conversion unit, insufficient intelligence, simple fault handling mechanism, slow response speed, poor system stability, and complex architecture in existing DC microgrid control systems in terms of stability, real-time performance, intelligence, adaptability, and security. It achieves efficient, stable, intelligent, and safe operation of the DC microgrid and adapts to the operational needs under various complex working conditions.

[0094] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0095] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above. Figure 1 The steps of the collaborative control method for DC microgrids in the illustrated method embodiment can be found in the method embodiment for specific implementation details, which will not be repeated here.

[0096] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the collaborative control method for DC microgrids in the illustrated method embodiment can be found in the method embodiment for specific implementation details, which will not be repeated here.

[0097] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and 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. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0099] 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.

[0100] In addition, the functional units in the various embodiments of this application 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.

[0101] 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 application, 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 application. 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.

[0102] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application 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 application. 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 application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A collaborative control method for a DC microgrid, characterized in that, The cooperative control method is applied to the cooperative control system of a DC microgrid, which includes an energy management unit and a power conversion unit; wherein the power conversion unit includes an edge intelligent control module and a low-level hard logic control module, and the cooperative control method includes: In response to the power-on of the collaborative control system, the energy management unit acquires the fault analysis data corresponding to the target DC microgrid and sends the fault analysis data to the edge intelligent control module, which then establishes a local expert database based on the fault analysis data. The energy management unit and the power conversion unit communicate in a hierarchical manner, so that the edge intelligent control module receives the macroscopic combination command sent by the energy management unit, and the edge intelligent control module determines the target power reference value corresponding to the target DC microgrid based on the macroscopic combination command and the local expert database. In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module adjusts the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid; In response to the power conversion unit detecting a fault in the target DC microgrid, the edge intelligent control module or the underlying hard logic control module performs corresponding fault handling based on the fault level corresponding to the fault.

2. The method according to claim 1, characterized in that, In response to the power-on of the collaborative control system, the energy management unit acquires fault analysis data corresponding to the target DC microgrid and sends the fault analysis data to the edge intelligent control module. The edge intelligent control module then establishes a local expert database based on the fault analysis data, including: In response to the power-on of the collaborative control system, the energy management unit obtains fault analysis data corresponding to the target DC microgrid from the cloud platform and sends the fault analysis data to the edge intelligent control module through the communication channel; wherein, the fault analysis data includes at least fault list information and fault knowledge graph information; The edge intelligent control module receives the fault analysis data and verifies and classifies the fault analysis data. The fault analysis data after verification and classification is loaded into the local storage medium to establish a local expert database.

3. The method according to claim 1, characterized in that, The energy management unit and the power conversion unit communicate in a hierarchical manner, enabling the edge intelligent control module to receive macroscopic combined commands sent by the energy management unit, including: The underlying hard logic control module collects telemetry data corresponding to the target DC microgrid and sends the telemetry data to the energy management unit through a preset hard channel; The energy management unit receives the telemetry data, generates a macroscopic combination command based on the telemetry data, and sends the macroscopic combination command to the edge intelligent control module through a preset soft channel; The macroscopic combined instruction is received by the edge intelligent control module.

4. The method according to claim 1, characterized in that, The step of determining the target power reference value corresponding to the target DC microgrid by the edge intelligent control module based on the macroscopic combined instructions and the local expert database includes: In response to receiving the macroscopic combined instruction, the edge intelligent control module acquires telemetry data in real time in the underlying hard logic control module; Based on the telemetry data, the macroscopic combined instructions are semantically parsed using the local expert database to determine the target power reference value of the target DC microgrid at the current moment, and the target power reference value is synchronized to the underlying hard logic control module.

5. The method according to claim 1, characterized in that, In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module adjusts the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid, including: In response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, the edge intelligent control module obtains the control topology knowledge information corresponding to the voltage change from the local expert database. Based on the control topology information, the duty cycle of the bidirectional current converter in the target DC microgrid is adjusted according to the target power reference value to adjust the power output value of the target DC microgrid and clamp the bus voltage of the target DC microgrid.

6. The method according to claim 1, characterized in that, When the fault level is either the first fault level or the second fault level, the fault handling performed by the edge intelligent control module or the underlying hard logic control module includes: Based on the fault information corresponding to the fault, the edge intelligent control module retrieves the response mode information from the local expert database. The fault is handled according to the response mode information, and the target DC microgrid is controlled to enter the derating self-healing mode. The fault handling chain information is sent to the energy management unit so that the energy management unit can update the cloud knowledge base in the cloud platform and synchronize it to each device in the target DC microgrid based on the fault handling chain information.

7. The method according to claim 1, characterized in that, When the fault level is the third fault level, the fault handling performed by the edge intelligent control module or the underlying hard logic control module includes: The underlying hard logic control module blocks the pulse width modulation signal corresponding to the target DC microgrid and disconnects the main contactor in the target DC microgrid to perform corresponding fault handling.

8. A collaborative control system for a DC microgrid, characterized in that, The collaborative control system includes an energy management unit and a power conversion unit; wherein, the power conversion unit includes an edge intelligent control module and a low-level hard logic control module; The energy management unit is used to respond to the power-on of the collaborative control system, acquire fault analysis data corresponding to the target DC microgrid, and send the fault analysis data to the edge intelligent control module. The edge intelligent control module is used to establish a local expert database based on the fault analysis data; receive macroscopic combination commands sent by the energy management unit through hierarchical communication between the energy management unit and the power conversion unit, and determine the target power reference value corresponding to the target DC microgrid based on the macroscopic combination commands and the local expert database; in response to the underlying hard logic control module detecting a voltage change in the target DC microgrid, adjust the power output value of the target DC microgrid based on the target power reference value to clamp the bus voltage of the target DC microgrid; and in response to the power conversion unit detecting a fault in the target DC microgrid, perform corresponding fault handling according to the fault level corresponding to the fault. The underlying hard logic control module is used to detect voltage changes in the target DC microgrid; in response to the power conversion unit detecting a fault in the target DC microgrid, it performs corresponding fault handling according to the fault level corresponding to the fault.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the cooperative control method of the DC microgrid as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the cooperative control method for a DC microgrid as described in any one of claims 1 to 7.

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