High-voltage SVG real-time harmonic detection and compensation system based on edge calculation

Abstract

The invention discloses a high-voltage SVG real-time harmonic detection and compensation system based on edge calculation, and belongs to the technical field of power equipment. The method comprises the steps of S1, enhancing environment perception and self-adaptive capability, S2, constructing a distributed cooperative control system, S3, improving robustness and self-healing capability of the system, S4, enhancing robustness and anti-interference capability of an algorithm, S5, managing intelligent edge computing resources, and S6, improving interoperability and standardization of equipment. The step S1 of enhancing the environment perception and self-adaptive capability comprises power grid topology intelligent identification, load characteristic online learning and multi-harmonic source cooperative suppression. Through localization processing and an efficient algorithm of the edge calculation unit, the harmonic detection delay is shortened, the compensation response time is shortened, compared with a traditional method, the harmonic compensation method has obvious improvement, harmonic changes in a power grid can be responded more quickly, the harmonic compensation precision is improved, and harmonic pollution in the power grid can be eliminated more accurately.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power equipment, and specifically relates to a high-voltage SVG real-time harmonic detection and compensation system based on edge computing. BACKGROUND

[0002] In the power system, harmonic pollution is a long-standing problem. With the wide application of power electronic equipment and the continuous increase of nonlinear loads, the harmonic content in the power grid is getting higher and higher, which seriously affects the power quality. Harmonics can cause equipment heating, increased loss, shortened life, and even misoperation and failure, which threatens the safe and stable operation of the power system.

[0003] Traditional harmonic detection and compensation methods mainly rely on centralized processing by a central controller. This method has problems such as slow response speed, low precision, and strong dependence on the central controller. In the context of the continuous expansion of the power grid and the increasing complexity of harmonic problems, the limitations of traditional methods are increasingly evident, and it is difficult to meet the high requirements of the power system for harmonic control.

[0004] In recent years, edge computing technology, as a new computing mode, has gradually attracted attention. Edge computing realizes the on-site processing and analysis of data by sinking computing tasks from the cloud to the network edge, and has the advantages of low delay, high bandwidth, and high reliability. Applying edge computing technology to the field of harmonic detection and compensation in the power system can effectively solve the problems existing in traditional methods and improve the real-time performance, reliability, and intelligent level of the system. SUMMARY

[0005] The purpose of the present application is to provide a high-voltage SVG real-time harmonic detection and compensation system based on edge computing to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical solutions: S1: enhancing environmental perception and adaptive ability, including power grid topology intelligent identification, load characteristic online learning, and multi-harmonic source collaborative suppression; S2: building a distributed collaborative control system, including a distributed control architecture based on edge computing, a collaborative control algorithm, and load balancing and resource optimization; S3: improving system robustness and self-healing ability, including strengthening component selection and reliability, online health monitoring, fault diagnosis, self-healing control, and fault-tolerant operation; S4: enhancing algorithm robustness and anti-interference ability, including robust algorithm design, anti-interference technology, extreme condition simulation and testing; S5: intelligent edge computing resource management, including resource monitoring and scheduling, task offloading, collaborative computing, and resource optimization configuration; S6: improving device interoperability and standardization, including standardized communication protocols, unified data models, open interfaces, and platforms; As a further preferred of the technical solution: the S1: enhance environmental perception and adaptive ability, wherein the power grid topology intelligent identification integrates advanced power grid topology identification algorithm, which can automatically identify the connection mode and parameters of the power grid, wherein the load characteristic online learning adopts machine learning algorithm to learn and model the load characteristics in the power grid, the system can identify different types of loads and their harmonic characteristics, and establish a load characteristic database, wherein the multi-harmonic source cooperative suppression, which is aimed at the case where multiple harmonic sources exist in the power grid, studies the multi-harmonic source cooperative suppression strategy; As a further preferred of the technical solution: the S2: build a distributed collaborative control system, wherein the distributed control architecture based on edge computing adopts a distributed control architecture, each SVG device is equipped with an edge computing unit, which can independently perform harmonic detection and local control, wherein the collaborative control algorithm realizes the optimization and coordination among multiple SVG devices, the algorithm can consider the overall harmonic condition of the power grid, the operating state and compensation capacity factors of each device, dynamically allocate compensation tasks, and realize the globally optimal harmonic compensation effect, wherein the load balancing and resource optimization collaborative control system can monitor the load conditions of each SVG device in real time, and dynamically balance the load according to the load conditions, to avoid the situation that some devices are overloaded while others are idle; As a further preferred of the technical solution: the S3: improve the robustness and self-healing ability of the system, wherein the strengthening of component selection and reliability strictly selects components in the system design stage, selects high-reliability and long-life components, and at the same time, adopts redundancy design, de-rating design and reliability design methods to improve the hardware reliability of the system, wherein the online health monitoring and fault diagnosis integrates an online health monitoring system to monitor the operating state of the SVG device in real time, including temperature, voltage, current, power factor and other key parameters, based on the monitoring data, a fault diagnosis algorithm is adopted to early warn and diagnose potential faults of the device, wherein the self-healing control and fault-tolerant operation enable the system to automatically isolate faults and reconstruct the system when a fault occurs, to maintain the basic function of the system; As a further preferred of the technical solution: the S4: enhance the robustness and anti-interference ability of the algorithm, wherein the robustness algorithm design is aimed at the harmonic detection and compensation demand under extreme working conditions, and designs an algorithm with stronger robustness, wherein the anti-interference technology improves the anti-interference ability of the system in complex electromagnetic environment, wherein the extreme working condition simulation and test comprehensively test and verify the system, through simulating power grid faults, voltage fluctuations, frequency deviations and other extreme working conditions, to test the performance and stability of the system under extreme conditions, and according to the test results, to optimize and improve the algorithm and control strategy; As a further preferred of the technical solution: the S5: intelligent edge computing resource management, wherein the resource monitoring and scheduling monitors the resource usage of the edge computing unit in real time, including CPU utilization, memory occupancy, storage space, wherein the task offloading and collaborative computing offloads part of the computing task to the cloud or other edge devices for processing, reduces the burden of the local edge computing unit, at the same time, explores the collaborative computing mode between multiple edge devices, realizes the sharing and optimized utilization of computing resources, wherein the resource optimization configuration optimizes the configuration of the edge computing resources according to the actual operation situation and demand of the system; As a further preferred of the technical solution: the S6: improving device interoperability and standardization, wherein the standardized communication protocol adopts a standard communication protocol, such as IEC61850, ModbusTCP / IP, realizes the interconnection and intercommunication between the SVG device and other power electronic devices, wherein the unified data model defines the data format and semantics of the interaction between devices, and the unified data model can ensure that the understanding and processing mode of different devices for data are consistent, and improves the interoperability between devices, wherein the open interface and platform facilitate the access of third-party devices and applications to the system; As a further preferred of the technical solution: in the S1: enhancing environment perception and adaptive ability, the power grid is identified as a radial or ring network, and line resistance, transformer ratio parameters can be identified, and rectifier load and frequency converter load parameters can be identified; As a further preferred of the technical solution: in the construction of a distributed collaborative control system, a collaborative control strategy based on a consistency algorithm is adopted, so that the compensation currents of multiple devices remain consistent in phase and amplitude, form a resultant force, and improve the compensation efficiency; in the self-healing control and fault-tolerant operation, when a power module fails, the system can automatically bypass it and redistribute the compensation task, ensuring that the system continues to provide harmonic compensation services and improving the fault tolerance and availability of the system; As a further preferred of the technical solution: in the robustness algorithm, a harmonic detection method based on robust statistics is adopted to reduce the influence of abnormal data on the detection result; an adaptive control algorithm is adopted, so that the control parameters can be automatically adjusted according to the changes of the power grid condition, and the stability of the control system is maintained.

[0007] Compared with the prior art, the present application has the following advantages: 1. The present application shortens the harmonic detection delay and the compensation response time through the localized processing of the edge computing unit and the efficient algorithm, has obvious improvement compared with the traditional method, can respond to the harmonic change in the power grid more quickly, and improves the accuracy of harmonic compensation, and can more accurately eliminate the harmonic pollution in the power grid.

[0008] 2.The system reliability and availability are greatly improved through the measures of strengthening component selection and reliability design, online health monitoring and fault diagnosis, self-healing control and fault-tolerant operation, etc., and the system can still maintain stable operation and provide continuous harmonic compensation services even in the case of equipment failure or extreme working conditions.

[0009] 3.The system has good scalability through distributed collaborative control and intelligent edge computing resource management, etc., and can easily integrate more SVG devices and other power electronic devices to meet the needs of different scale power grids, and through load balancing and resource optimization, intelligent edge computing resource management, etc., the energy efficiency of the system is optimized, the device energy consumption is reduced, and at the same time, since the system can more accurately detect and compensate harmonics, unnecessary energy loss is reduced, further reducing the operating cost. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 FIG. 1 is a structural diagram of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure 2 FIG. 2 is a flowchart of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure One Figure 3 FIG. 3 is a flowchart of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure Two Figure 4 FIG. 4 is a flowchart of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure Three Figure 5 FIG. 5 is a flowchart of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure Four Figure 6 FIG. 6 is a flowchart of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure Five Figure 7 FIG. 7 is a flowchart of a high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to the present application; Figure Six DETAILED DESCRIPTION

[0011] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. ​​​​​​

[0012] Static Var Generator (SVG) is a dynamic reactive power compensation device based on power electronic technology (usually using IGBT and other fully controlled devices). It can quickly, continuously and accurately emit or absorb reactive power, thereby maintaining system voltage stability, improving power factor and improving power quality.

[0013] Correspondingly, SVG can be mainly applied in power systems, new energy, metallurgy, manufacturing and mining, etc. The following mainly introduces the application of SVG in power systems and new energy scenarios.

[0014] In the power system, SVG can be applied in voltage stability control, wind farm / photovoltaic power station grid connection point, substation and power quality management node, etc. to improve the stability and reliability of the power system.

[0015] Specifically, when SVG is applied in voltage stability control, SVG can be installed at the end of long-distance transmission lines, load centers or weak nodes to provide dynamic reactive power support, suppress voltage drop or rise, and improve system stability and transmission capacity.

[0016] When SVG is applied in wind farm / photovoltaic power station grid connection point, due to the intermittency and volatility of new energy output, the voltage at the grid connection point fluctuates. SVG can provide fast reactive power regulation to meet the voltage and power factor requirements of the grid connection guide, and is the key to solving the reactive power demand during "low penetration / high penetration".

[0017] When SVG is applied in substation, it can compensate for the reactive power loss in the station and maintain the bus voltage within the qualified range.

[0018] When SVG is applied in power quality management nodes, it can be installed upstream of sensitive loads or weak points of power quality to comprehensively solve voltage fluctuations, flicker, imbalance and other problems.

[0019] In the new energy field, SVG can be applied in wind power plants, photovoltaic power stations and energy storage power stations to stabilize the voltage and ensure the stability and reliability of the power system connected to the power plant.

[0020] Specifically, when SVG is applied in wind power plants, wind turbines (especially double-fed and full-power converter types) themselves absorb reactive power, and wind speed changes cause output fluctuations, causing voltage fluctuations and power factor problems at the grid connection point. Therefore, SVG is a standard configuration to meet grid specifications (such as providing reactive power support during low voltage ride-through) and ensure voltage stability in the station.

[0021] When applied in photovoltaic power stations, SVG is usually operated at unity power factor based on photovoltaic inverters, but the rapid fluctuation of output caused by changes in sunlight and cloud cover affects the voltage at the grid connection point. Accordingly, SVG provides dynamic reactive power compensation to stabilize the voltage and meet the grid connection requirements.

[0022] When applied in energy storage power stations, SVG can provide reactive power support to maintain voltage stability when the energy storage converter switches between charging and discharging modes.

[0023] Therefore, the working environment of SVG is mainly concentrated in situations where there is a rapid and large fluctuation in reactive power demand, and there are high requirements for voltage stability, power factor or power quality. From heavy industry with severe impact loads (such as electric arc furnaces and rolling mills) to fluctuating new energy field stations (wind power and photovoltaic) grid connection, to power system nodes and important facilities sensitive to power quality (data centers and rail transit), SVG is a key technology and equipment for solving dynamic reactive power problems, improving grid stability and power quality in modern power systems. Its application scenarios are becoming increasingly widespread as the penetration rate of power electronic loads and new energy increases.

[0024] Embodiment: Please refer to Figures 1-7 As shown in the figure, the present application provides a high-voltage SVG real-time harmonic detection and compensation system based on edge computing: S1: enhance environmental perception and adaptive ability, S2: build a distributed collaborative control system, S3: improve system robustness and self-healing ability, S4: enhance algorithm robustness and anti-interference ability, S5: intelligent edge computing resource management, S6: improve device interoperability and standardization. S1: Enhance environmental perception and adaptive ability, including power grid topology intelligent identification, load characteristic online learning and multi-harmonic source collaborative suppression. The power grid topology intelligent identification integrates advanced power grid topology identification algorithm, which can automatically identify the connection mode and parameters of the power grid. Based on the identification result, the system can dynamically adjust the harmonic detection and control strategy to adapt to different power grid topologies. The load characteristic online learning adopts machine learning algorithm to learn and model the load characteristics in the power grid. The system can identify different types of loads and their harmonic characteristics, and establish a load characteristic database. The multi-harmonic source collaborative suppression can select the optimal algorithm and control parameters according to the load type during harmonic detection and compensation, improving the detection accuracy and compensation effect. The multi-harmonic source collaborative suppression strategy is studied for the case of multiple harmonic sources in the power grid. The system can identify the contribution of each harmonic source and develop targeted compensation schemes to avoid mutual influence between harmonic sources and improve overall harmonic suppression effect. The power grid topology identification algorithm based on graph theory is used to construct the power grid topology model by combining wide-area measurement system data and node voltage phase angle difference and line flow direction. The algorithm supports automatic identification of radial, loop and mixed topologies with an identification accuracy of more than 99.5%. During power distribution network reconstruction, fault location and distributed power source access, the protection strategy is dynamically adjusted. In this embodiment, specifically: S2: Construct a distributed collaborative control system including a distributed control architecture based on edge computing, a collaborative control algorithm, and load balancing and resource optimization. The distributed control architecture based on edge computing uses a distributed control architecture, with each SVG device equipped with an edge computing unit that can independently perform harmonic detection and local control. Devices interact and collaboratively control through high-speed communication networks. The collaborative control algorithm optimizes coordination between multiple SVG devices, taking into account the overall harmonic condition of the power grid, the operating state and compensation capacity of each device, dynamically allocating compensation tasks to achieve globally optimal harmonic compensation effect. The load balancing and resource optimization collaborative control system can monitor the load of each SVG device in real time and dynamically balance the load according to the load condition to avoid overloading of some devices while others are idle. The system can optimize device operating parameters to reduce device energy consumption and improve overall system efficiency. The model predictive control algorithm is used to predict the harmonic trend in the next 0.1 seconds and adjust the compensation current in advance. In this embodiment, specifically: S3: improve system robustness and self-healing ability, including strengthening component selection and reliability, online health monitoring and fault diagnosis and self-healing control and fault-tolerant operation, wherein the component selection and reliability is strictly screened in the system design stage, and high reliability and long life components are selected, at the same time, the redundancy design, the reduced design and the reliability design method are adopted to improve the hardware reliability of the system, and the dual power supply redundancy design is adopted to ensure that the system can still work normally when one power supply fails, wherein the online health monitoring and fault diagnosis integrate the online health monitoring system, which monitors the running state of the SVG device in real time, including temperature, voltage, current, power factor and other key parameters, based on the monitoring data, the fault diagnosis algorithm is adopted to early warning and diagnose the potential fault of the device, and the fault diagnosis method based on artificial intelligence is adopted, through the learning of historical fault data, the fault diagnosis model is established, and the rapid and accurate diagnosis of the device fault is realized, wherein the self-healing control and fault-tolerant operation can automatically isolate faults and reconstruct the system when a fault occurs, and keep the basic function of the system running, when a power module fails, the system can automatically bypass it and redistribute the compensation task to ensure that the system continues to provide harmonic compensation service, improve the fault-tolerant capability and availability of the system, when the power module fails, it is bypassed through the solid state relay within 50us, and the remaining healthy module ensures that the total compensation capacity is not less than 90% of the rated value through current redistribution algorithm; In this embodiment, specifically: S4: enhance algorithm robustness and anti-interference ability, including robust algorithm design, anti-interference technology and extreme condition simulation and test, wherein the robust algorithm design is designed for the harmonic detection and compensation demand under extreme conditions, and the algorithm with stronger robustness is designed, wherein the anti-interference technology improves the anti-interference ability of the system in complex electromagnetic environment, and the digital filtering technology is adopted to suppress the noise interference in the power grid; electromagnetic compatibility design can be adopted to improve the electromagnetic interference degree of the system, wherein the extreme condition simulation and test comprehensively tests and verifies the system, through simulating power grid fault, voltage fluctuation, frequency offset and other extreme conditions, the performance and stability of the system under extreme conditions are tested, and the algorithm and control strategy are optimized and improved according to the test result, the robust statistical method based on median filtering is adopted to effectively suppress pulse noise interference, and the fuzzy PID algorithm is adopted to adjust the control parameters in real time to adapt to the change of power grid impedance; In this embodiment, specifically: S5: intelligent edge computing resource management includes resource monitoring and scheduling, task offloading and collaborative computing, and resource optimization configuration, wherein the resource monitoring and scheduling monitors the resource usage of the edge computing unit in real time, including CPU utilization, memory occupancy, storage space, based on the resource monitoring data, adopts intelligent scheduling algorithm, dynamically allocates computing resources, ensures that critical tasks can obtain sufficient resources in priority, adopts priority scheduling algorithm based on, according to the urgency and importance of the task, allocates resources; can adopt load balancing based scheduling algorithm, evenly distribute tasks to different computing resources, improve resource utilization, wherein task offloading and collaborative computing offloads part of the computing task to the cloud or other edge devices for processing, reduces the burden of the local edge computing unit, at the same time, explores the collaborative computing mode among multiple edge devices, realizes the sharing and optimization utilization of computing resources, adopts edge computing federated learning framework, realizes the model training and parameter sharing among multiple edge devices, improves the accuracy and efficiency of the algorithm, wherein the resource optimization configuration optimizes the configuration of the edge computing resource according to the actual operation and demand of the system, dynamically adjusts the operation frequency and core number of the edge computing unit according to the complexity of the harmonic detection and compensation task; can optimize the data transmission strategy according to the network bandwidth, reduce the data transmission delay: the priority of harmonic detection task is the highest, the priority of data storage task is the lowest, based on dynamic voltage frequency adjustment technology, adjusts the CPU frequency according to the load, offloads the model training task to the cloud, only retains the inference module locally, multiple edge devices cooperatively train the harmonic prediction model through FedAvg algorithm; In this embodiment, specifically: S6: improving device interoperability and standardization includes standardized communication protocol, unified data model, and open interface and platform, wherein the standardized communication protocol adopts standard communication protocol, such as IEC61850, ModbusTCP / IP, realizes the interconnection and intercommunication between SVG device and other power electronic devices, wherein the unified data model defines the data format and semantics of the interaction between devices, the unified data model can ensure that different devices understand and process data in the same way, improve the interoperability between devices, wherein the open interface and platform facilitate third-party devices and applications to access the system, the open interface and platform can promote the integration and collaborative work between devices, build a more open and flexible power system, provide RESTfulAPI interface, support third-party applications to call device control, data query and other functions, compatible with mainstream energy management system, realize plug and play; In this embodiment, specifically: S1: enhancing environment perception and adaptive ability, identifying the power grid as radial or loop type, and identifying line resistance, transformer ratio parameters, and can identify rectifier load, frequency converter load parameters; In this embodiment, specifically: the construction of distributed collaborative control system adopts collaborative control strategy based on consistency algorithm, so that the compensation currents of multiple devices are consistent in phase and amplitude, a resultant force is formed, and the compensation efficiency is improved; In this embodiment, specifically: when a power module fails in self-healing control and fault-tolerant operation, the system can automatically bypass it and redistribute the compensation task, ensuring that the system continues to provide harmonic compensation services and improving the fault tolerance and availability of the system. In this embodiment, specifically: in the robust algorithm, a harmonic detection method based on robust statistics is used to reduce the influence of abnormal data on the detection result; an adaptive control algorithm is used to automatically adjust the control parameters according to the changes in the power grid conditions, maintaining the stability of the control system.

[0025] Working principle or structural principle: After the system starts, it first enters the environmental perception stage. Through the edge computing unit integrated in the SVG device, it runs advanced power grid topology identification algorithms to analyze the connection mode and key parameters of the power grid in real time. At the same time, it uses machine learning algorithms to learn the load characteristics of the power grid online, identifies different types of loads (such as rectifier loads and frequency converter loads), and establishes a load characteristic database. Based on the perceived power grid topology and load characteristics, the system dynamically adjusts the harmonic detection and control strategy, such as selecting the optimal harmonic detection algorithm and control parameters for specific load types to improve detection accuracy and compensation effect. When there are multiple harmonic sources in the power grid, the system starts the multi-harmonic source collaborative suppression strategy, analyzes the contribution of each harmonic source, and develops a targeted compensation scheme to avoid mutual influence between harmonic sources, thereby improving overall harmonic suppression effect. In the scenario of multiple SVG devices working together, the system builds a distributed control architecture based on edge computing. Each SVG device is equipped with an edge computing unit that can independently perform harmonic detection and local control. The devices interact with each other through a high-speed communication network, run collaborative control algorithms, and use a consensus algorithm-based collaborative control strategy to ensure that the compensation currents of multiple devices are consistent in phase and amplitude, forming a resultant force and improving compensation efficiency. The collaborative control system also monitors the load of each SVG device in real time and performs dynamic load balancing based on the load to avoid overloading some devices while others are idle. At the same time, it optimizes device operating parameters to reduce device energy consumption and improve overall system efficiency. During system design, high-reliability and long-life components are strictly selected, and redundancy design, derating design, and other reliability design methods are used to improve system hardware reliability. An online health monitoring system is integrated to monitor the operating state of the SVG device, including temperature, voltage, current, power factor, and other key parameters. Based on the monitoring data, an artificial intelligence-based fault diagnosis algorithm is used to early warn and diagnose potential device failures. When the system fails, the self-healing control and fault-tolerant operation mechanism starts, allowing the system to automatically isolate faults and reconfigure the system to maintain basic functionality. For example, when a power module fails, the system can automatically bypass it and redistribute compensation tasks to ensure continuous harmonic compensation service and improve system fault tolerance and availability. Robust algorithms are designed for extreme working conditions, such as using a robust statistical harmonic detection method to reduce the impact of abnormal data on detection results.Adopting adaptive control algorithm, the control parameter can be automatically adjusted according to the change of power grid condition, keep the stability of control system, improve the anti-interference ability of system in complex electromagnetic environment, through the extreme condition simulation and test platform, the system is tested and verified comprehensively, simulate power grid fault, voltage fluctuation, frequency deviation and other extreme conditions, test the performance and stability of the system under extreme conditions, and optimize and improve the algorithm and control strategy according to the test result, real-time monitoring the resource usage of edge computing unit, including CPU utilization, memory occupancy, storage space, etc., based on resource monitoring data, using intelligent scheduling algorithm, dynamically allocate computing resources, ensure that critical tasks can get enough resources in priority, for example, using priority scheduling algorithm, allocate resources according to the urgency and importance of tasks; Using load balancing based scheduling algorithm, evenly distribute tasks to different computing resources, improve resource utilization, using edge computing federated learning framework, realize model training and parameter sharing between multiple edge devices, improve the accuracy and efficiency of algorithm, according to the complexity of harmonic detection and compensation task, dynamically adjust the operation frequency and core number of edge computing unit; According to the network bandwidth, optimize the data transmission strategy, reduce the data transmission delay, adopt standard communication protocol to realize the interconnection and interoperation between SVG device and other power electronic devices, establish a unified data model, define the data format and semantics of the interaction between devices, ensure the consistency of data understanding and processing mode between different devices, improve the interoperability between devices, provide open interface and platform, facilitate third-party devices and applications to access the system, the open interface and platform can promote the integration and cooperation between devices, build a more open and flexible power system.

[0026] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in a descriptive sense only and not for purposes of limitation. The scope of the present application should be defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference numerals in the claims are intended to correspond to structures unique to the embodiment in which the claim is in effect, and are not intended to correspond, on their own, to structures in another embodiment.

[0027] In addition, it should be understood that, although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be combined appropriately to form other embodiments that those skilled in the art can understand.

Claims

1. A high-voltage SVG real-time harmonic detection and compensation system based on edge computing, characterized in that: include: S1: Enhanced environmental perception and adaptive capabilities, including intelligent identification of grid topology, online learning of load characteristics, and coordinated suppression of multiple harmonic sources; S2: Build a distributed collaborative control system, including a distributed control architecture based on edge computing, collaborative control algorithms, load balancing, and resource optimization. S3: Improve system robustness and self-healing capabilities, including strengthening component selection and reliability, online health monitoring and fault diagnosis, self-healing control, and fault-tolerant operation. S4: Enhance algorithm robustness and anti-interference capabilities, including robust algorithm design, anti-interference technology, and extreme working condition simulation and testing; S5: Intelligent edge computing resource management, including resource monitoring and scheduling, task offloading and collaborative computing, and resource optimization and configuration; S6: Improve device interoperability and standardization, including standardized communication protocols, unified data models, and open interfaces and platforms.

2. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 1 is characterized by: Said S1: Enhances environmental perception and self-adaptation capabilities, wherein the intelligent recognition of power grid topology integrates advanced power grid topology recognition algorithms, which can automatically identify the connection mode and parameters of the power grid. The online learning of load characteristics adopts machine learning algorithms to perform online learning and modeling of the load characteristics in the power grid. The system can identify different types of loads and their harmonic characteristics, and establish a load characteristic database. Among them, the collaborative suppression of multiple harmonic sources, wherein the collaborative suppression of multiple harmonic sources aims at the situation where there are multiple harmonic sources in the power grid, and studies the collaborative suppression strategy of multiple harmonic sources.

3. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 2 is characterized by: S2: Constructing a distributed collaborative control system, wherein a distributed control architecture based on edge computing is adopted. Each SVG device is equipped with an edge computing unit, capable of independent harmonic detection and local control. The collaborative control algorithm realizes optimized coordination among multiple SVG devices. The algorithm can consider the overall harmonic status of the power grid, the operating status and compensation capacity of each device, dynamically allocate compensation tasks, and achieve a globally optimal harmonic compensation effect. The load balancing and resource optimization collaborative control system can monitor the load status of each SVG device in real time and perform dynamic load balancing based on the load status to avoid overloading some devices while other devices are idle.

4. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 3 is characterized by: S3: Improve system robustness and self-healing capabilities, including strengthening component selection and reliability. During the system design phase, strictly screen components and select components with high reliability and long life. At the same time, adopt redundant design and derating design reliability design methods to improve the hardware reliability of the system. Online health monitoring and fault diagnosis integrate an online health monitoring system to monitor the operating status of SVG equipment in real time, including key parameters such as temperature, voltage, current, and power factor. Based on the monitoring data, a fault diagnosis algorithm is used to provide early warning and diagnosis of potential equipment failures. Self-healing control and fault-tolerant operation enable the system to automatically isolate faults and reconstruct the system when a failure occurs, thereby maintaining the basic function operation of the system.

5. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 4 is characterized by: Said S4: Enhance the robustness and anti-interference capability of the algorithm, wherein the robustness algorithm is designed to meet the requirements of harmonic detection and compensation under extreme working conditions, and a more robust algorithm is designed, wherein the anti-interference technology improves the anti-interference capability of the system in a complex electromagnetic environment, wherein the extreme working condition simulation and testing conducts a comprehensive test and verification of the system, and by simulating extreme working conditions such as power grid failures, voltage fluctuations, and frequency offsets, the performance and stability of the system under extreme conditions are tested, and the algorithm and control strategy are optimized and improved according to the test results.

6. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 5 is characterized by: Said S5: Intelligent edge computing resource management, wherein resource monitoring and scheduling monitor the resource usage of edge computing units in real time, including CPU utilization, memory occupancy, and storage space, wherein task offloading and collaborative computing offload part of the computing tasks to the cloud or other edge devices for processing, reducing the burden on local edge computing units, and at the same time, exploring collaborative computing modes between multiple edge devices to realize the sharing and optimal utilization of computing resources, wherein resource optimization configuration optimizes the configuration of edge computing resources according to the actual operation status and needs of the system.

7. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 6 is characterized by: S6: Improve equipment interoperability and standardization, where standardized communication protocols adopt standard communication protocols, such as IEC61850 and Modbus TCP / IP, to achieve interconnection between SVG equipment and other power electronic equipment. The unified data model defines the data format and semantics of the interaction between devices. The unified data model can ensure that different devices have consistent understanding and processing methods of data, thereby improving interoperability between devices. The open interface and platform facilitate the access of third-party devices and applications to the system.

8. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 7 is characterized by: The S1: enhancing the environmental perception and self-adaptation capability can identify whether the power grid is radial or ring, and identify the line impedance and transformer ratio parameters, and can also identify the rectifier load and inverter load parameters.

9. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 8 is characterized by: A distributed collaborative control system is constructed using a collaborative control strategy based on a consistency algorithm, which ensures that the compensation currents of multiple devices remain consistent in phase and amplitude, forming a combined force and improving compensation efficiency. During self-healing control and fault-tolerant operation, when a power module fails, the system can automatically bypass it and reallocate compensation tasks to ensure that the system continues to provide harmonic compensation services, thereby improving the system's fault tolerance and availability.

10. The high-voltage SVG real-time harmonic detection and compensation system based on edge computing according to claim 9 is characterized by: The robustness algorithm adopts a harmonic detection method based on robust statistics to reduce the impact of abnormal data on the detection results; the adaptive control algorithm is adopted to enable the control parameters to be automatically adjusted according to changes in the power grid conditions to maintain the stability of the control system.

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