Intelligent monitoring ball enhanced distributed power supply coordination pre-synchronization control method and system

Abstract

The invention relates to the technical field of distributed energy grid-connected control, in particular to an intelligent monitoring ball enhanced distributed power supply coordination pre-synchronization control method and system, and the method comprises the steps: deploying a master intelligent monitoring ball at a grid-connected point, deploying slave intelligent monitoring balls at the output ends of all distributed power supplies, and forming a star-shaped sensing network; based on a multi-objective optimization algorithm, calculating a pre-synchronization target parameter and an adjustment weight of each distributed power supply, and generating a coordination control instruction; the virtual synchronous motor module receives the coordination control instruction, dynamically adjusts a virtual inertia and a damping coefficient, and adjusts output voltage, frequency and phase through a pre-synchronization execution unit; and after the synchronization reaches the standard, sequentially closing the grid-connected switches according to a preset power supply response priority sequence to complete the grid-connected action. According to the invention, a technical architecture of global monitoring-coordinated decision-precise execution-closed loop optimization is constructed, so that the stability and precise control problem during high-permeability distributed energy grid connection is solved.

Description

Technical Field

[0001] This invention relates to the field of distributed energy grid connection control technology, specifically to distributed power generation cluster grid connection scenarios such as photovoltaic, wind power, and energy storage, and particularly to a distributed power generation coordination and pre-synchronization control method and system enhanced by intelligent monitoring spheres. Background Technology

[0002] With the increasing proportion of distributed energy in the power system, traditional distributed power sources (such as photovoltaic inverters and wind power converters) are prone to grid frequency fluctuations, voltage instability, and even cascading failures after grid connection due to their lack of rotational inertia and damping characteristics. To solve this problem, Virtual Synchronous Generator (VSG) technology has emerged. Its core is to simulate the inertia, damping, speed regulation, and voltage regulation characteristics of traditional synchronous generators through control algorithms, providing inertial support for the grid. It has become the mainstream technology direction for distributed power grid connection control.

[0003] The publicly disclosed related technologies and research results include: Chinese invention patent CN111245030A discloses a control method for a distributed generation virtual synchronous machine. This method detects the grid frequency deviation, dynamically adjusts the equivalent inertial time constant of the virtual synchronous machine, and formulates an adaptive load reduction strategy to improve the primary frequency regulation capability of the system. However, it does not involve the pre-synchronization coordination control before multiple distributed power sources are connected to the grid, and only focuses on the frequency regulation optimization of a single power source. Chinese invention patent CN110247434B proposes a grid-connected control method based on the self-adjusting inertia of a virtual synchronous motor. The method determines the optimization range of inertia parameters through load switching simulation and realizes the segmented adaptive adjustment of inertia parameters. However, this method relies on offline simulation data, lacks the ability to respond quickly to real-time disturbances in the power grid, and does not consider the parameter conflict problem when multiple power sources are connected in parallel. The journal article "Application Research of Virtual Synchronous Motor Technology in Distributed Power Generation Grid Connection" (Automation of Electric Power Systems, Vol. 42, No. 15, 2018, Authors: Li Ming, Wang Qiang, et al., Pages: 12-18) points out that existing VSG control strategies generally adopt fixed inertia coefficients and damping coefficients, or make local adjustments based on a single grid parameter (such as frequency deviation), which makes it difficult to balance dynamic response speed and system stability. In the event of sudden load changes or grid disturbances, phenomena such as power oscillation and voltage overshoot are likely to occur. The industry standard GB / T38940-2020 Technical Requirements for Virtual Synchronous Motors clearly defines the basic performance indicators of VSGs (such as frequency response time and voltage regulation accuracy), but does not specify the specific implementation method of multi-power source coordination pre-synchronization. The application of existing technologies in multi-power source cluster grid connection scenarios still lacks unified standards.

[0004] Furthermore, existing pre-synchronization control for distributed power grid connection mostly adopts a "local detection-independent adjustment" mode, where each power source detects the voltage, frequency, and phase at the grid connection point through its own deployed sensors and independently completes parameter matching before grid connection. For example, a distributed power source grid connection pre-synchronization device disclosed in Chinese utility model patent CN212343567U only collects data through local voltage transformers and frequency detectors to adjust inverter output parameters, without considering the coupling effects between multiple power sources.

[0005] In summary, the core shortcomings of existing technologies mainly include: Insufficient pre-synchronization accuracy and high risk of grid connection impact: Existing pre-synchronization control relies on local detection data from a single node, failing to acquire the global operating status of the grid and other power sources. This leads to problems such as excessive phase difference and voltage amplitude deviation when each power source adjusts independently (in practical applications, the phase difference often exceeds 5° and the voltage deviation exceeds 8%). This results in a large inrush current (typically 1.5-2 times the rated current) at the moment of grid connection, which can damage the inverter's power electronic components and potentially cause grid voltage fluctuations. The causes are: a lack of a global parameter monitoring mechanism; the data collected by local sensors is limited in scope and cannot reflect the mutual influence when multiple power sources are connected in parallel; and a multi-power source coordination and matching strategy has not been established. The challenge lies in achieving real-time synchronous acquisition and accurate coordination of multi-node parameters to avoid adjustment conflicts.

[0006] Dynamic response lag and weak disturbance rejection capability: Existing VSGs mostly use fixed values ​​for inertia and damping coefficients or piecewise adjustment values ​​based on offline simulation. When faced with real-time disturbances such as sudden changes in grid frequency and drastic load fluctuations, parameter adjustment lags (response time usually exceeds 300ms), easily leading to power oscillations or voltage instability. As experimental data from the aforementioned paper "Automation of Electric Power Systems" shows, when a fixed-parameter VSG experiences a sudden 0.5Hz drop in grid frequency, the power oscillation lasts for more than 1 second. The cause lies in the lack of real-time monitoring of grid conditions and online parameter optimization capabilities; the parameter adjustment logic relies on preset rules and cannot adapt to dynamic changes in the grid. The challenge lies in how to achieve rapid adaptive adjustment of inertia-damping parameters while ensuring system stability, balancing response speed and oscillation suppression effect.

[0007] The lack of coordination among multiple power sources leads to poor stability in cluster operation: When multiple distributed power sources operate in parallel, each power source independently performs pre-synchronization and post-grid connection stability control, lacking a unified coordination mechanism. This results in some power sources over-regulating while others under-regulate, reducing the overall inertial support capacity of the system and even causing a "negative damping" phenomenon (such as photovoltaic and wind power sources regulating in opposite directions, canceling each other's inertial contributions). The cause lies in the lack of a global coordinated control model, fragmented control objectives for each power source, and a lack of weight allocation and conflict avoidance mechanisms. The challenge lies in how to construct a coordination algorithm that takes into account the characteristics of each power source (such as energy storage SOC and photovoltaic irradiance) to achieve multi-objective optimization.

[0008] High parameter sensitivity and limited applicability: Existing VSG control parameters (such as inertia coefficient and damping coefficient) are highly dependent on specific grid operating conditions. When the penetration rate of distributed power sources changes (e.g., from 20% to 50%) or the type of power source connected changes (e.g., adding energy storage power sources), the original parameters are prone to failure and need to be recalibrated through simulation or experiments, making them unsuitable for complex and ever-changing grid connection scenarios. The cause lies in the parameter design's failure to consider the dynamic characteristics after multi-power source coupling, resulting in a lack of adaptive robustness. The challenge lies in how to construct a universal coordinated control strategy that is independent of specific operating conditions, thereby improving the versatility of the technical solution.

[0009] The aforementioned problems are interconnected, making it difficult for existing virtual synchronous motor control technology to meet the safety and stability requirements of high penetration and multi-type distributed power cluster grid connection, thus limiting the large-scale promotion and application of distributed energy. Therefore, there is an urgent need for a pre-synchronous control scheme that can achieve global monitoring, precise coordination, and adaptive adjustment. Summary of the Invention

[0010] This invention aims to address the stability and precise control challenges of grid-connected distributed energy resources with high penetration rates. By constructing a star-shaped sensing network based on an intelligent monitoring sphere, it achieves system-wide data acquisition from a single source. Furthermore, a multi-objective optimization algorithm generates coordinated control commands, enabling synchronized parameter adjustments among distributed power sources under virtual synchronous motor control. This ultimately reduces grid connection impact and improves system stability. This method pays particular attention to regulation conflicts caused by differences in the characteristics of different power sources, achieving optimal coordinated control through dynamic weight allocation. It is applicable to power systems with multiple power sources operating in parallel, such as distribution networks and microgrids.

[0011] According to one aspect of the present invention, a method for coordinated pre-synchronization control of distributed power sources enhanced by intelligent monitoring spheres is provided, comprising: deploying a master intelligent monitoring sphere at the grid connection point and a slave intelligent monitoring sphere at the output end of each distributed power source to form a star-shaped sensing network; a coordination control center receiving homogeneous data collected by the star-shaped sensing network, calculating the pre-synchronization target parameters and adjustment weights of each distributed power source based on a multi-objective optimization algorithm, generating coordination control commands, and allocating them to a virtual synchronous motor module; the virtual synchronous motor module receiving the coordination control commands, dynamically adjusting the virtual inertia and damping coefficient based on an inertia-damping adaptive adjustment algorithm, and simultaneously adjusting the output voltage, frequency, and phase through a pre-synchronization execution unit to synchronize the output of each distributed power source with the grid parameters; after synchronization is achieved, closing the grid connection switch sequentially according to a preset power source response priority order to complete the grid connection action.

[0012] In some optional implementations of certain embodiments, both the master intelligent monitoring sphere and the slave intelligent monitoring sphere integrate an electrical quantity acquisition module, an environmental monitoring module, a video recognition module, a time synchronization module, and a wireless communication module for synchronously acquiring power grid parameters, power supply operating status parameters, and environmental parameters.

[0013] In some optional implementations of certain embodiments, the coordination control center receives the same source data collected by the star-shaped sensing network, calculates the pre-synchronization target parameters and adjustment weights of each distributed power source based on a multi-objective optimization algorithm, and generates coordination control instructions. This includes: the coordination control center receiving the same source data collected by the star-shaped sensing network and performing filtering and weighted fusion processing to generate global data; and based on the global data, calculating the pre-synchronization target parameters and adjustment weights of each distributed power source through a multi-objective optimization algorithm to generate coordination control instructions.

[0014] In some alternative implementations of certain embodiments, the adjustment weights are nonlinear functions of the state of charge, light intensity, and wind speed of each distributed power source, in order to minimize grid-connected inrush current and avoid adjustment conflicts.

[0015] In some optional implementations of certain embodiments, the virtual inertia and damping coefficient are adjusted based on the pre-synchronization target parameters contained in the coordination control instructions issued by the coordination control center, and the virtual synchronous motor modules of each distributed power source achieve dual-layer adaptive adjustment based on the online fitting parameters of local frequency and power deviation.

[0016] In some optional implementations of certain embodiments, the preset power supply response priority is: energy storage power supply takes precedence over photovoltaic power supply, and photovoltaic power supply takes precedence over wind power supply.

[0017] In some optional implementations of some embodiments, the method further includes: after the grid connection operation is completed, continuously collecting source data from the star sensing network, dynamically adjusting the adjustment weight and pre-synchronization target parameters based on closed-loop feedback to maintain system stability, and optimizing power allocation according to power supply operating status parameters.

[0018] According to a second aspect of the present invention, a system is provided comprising: an intelligent monitoring sphere module for deploying a master intelligent monitoring sphere at the grid connection point and slave intelligent monitoring spheres at the output terminals of each distributed power source to form a star-shaped sensing network; a coordination control center, comprising a data processing unit, a coordination decision unit, and an instruction allocation unit, for receiving homogeneous data collected by the star-shaped sensing network, calculating the pre-synchronization target parameters and adjustment weights of each distributed power source based on a multi-objective optimization algorithm, generating coordination control instructions, and allocating them to a virtual synchronous motor module, and, after synchronization is achieved, sequentially closing the grid connection switch according to a preset power source response priority order to complete the grid connection action; a virtual synchronous motor module, deployed in the grid-connected inverters of each distributed power source, having a built-in inertia-damping adaptive adjustment unit for receiving the coordination control instructions and dynamically adjusting the virtual inertia and damping coefficient based on the inertia-damping adaptive adjustment algorithm; and a pre-synchronization execution unit, comprising a voltage regulation component, a frequency regulation component, and a phase regulation component, connected in series between the inverter and the grid connection switch, for adjusting the output voltage, frequency, and phase to synchronize the output of each distributed power source with the grid parameters.

[0019] According to a third aspect of the present invention, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method described above.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the steps of the above-described method.

[0021] The intelligent monitoring sphere-enhanced distributed power supply coordination and pre-synchronization control method and system provided in this application have the following beneficial effects: 1. Improved pre-synchronization accuracy: Through global monitoring and coordinated decision-making, the voltage / frequency / phase matching error is ≤2% / 0.1Hz / 1°, which is significantly reduced compared to the existing technology (error ≥5% / 0.5Hz / 5°). The grid connection inrush current is ≤1.2 times the rated current, protecting power electronic devices.

[0022] 2. Improved dynamic response: Through the adaptive inertia-damping adjustment algorithm, the power grid disturbance response time is ≤50ms, which is 80% faster than the traditional fixed parameter control (≥300ms), effectively suppressing power oscillation.

[0023] 3. Enhanced efficiency through multi-power supply coordination: By adopting a weight allocation and conflict avoidance mechanism, conflicts in the adjustment of multiple power supplies are avoided, the overall stability of the system is improved by 40%, and the inertial support of the cluster is fully utilized.

[0024] 4. Expanded application scenarios: No offline parameter calibration is required, adaptable to various power sources such as photovoltaic, wind power, and energy storage, as well as scenarios with different penetration rates, and its robustness is superior to existing control solutions that depend on operating conditions.

[0025] 5. Reduced operation and maintenance costs: Closed-loop control reduces manual parameter adjustments, lowers equipment failure rate by 30%, extends component lifespan, and reduces operation and maintenance investment. Attached Figure Description

[0026] Figure 1 This is the overall flowchart of the present invention.

[0027] Figure 2 This is a framework diagram of the virtual synchronous motor multi-loop control system of the present invention.

[0028] Figure 3 This is a flowchart illustrating the internal or phased determination criteria of the virtual synchronous motor grid-connected pre-synchronization control logic of the present invention.

[0029] Figure 4 This is a system framework diagram of the present invention.

[0030] Figure 5 This is a schematic diagram illustrating the deployment configuration and sensing range of the intelligent monitoring ball of the present invention in a distributed power supply site.

[0031] Figure 6 This is a schematic diagram of an electronic device. Detailed Implementation

[0032] 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] Example 1 Reference Appendix Figure 1 This application provides a distributed power supply coordination pre-synchronization control method enhanced by intelligent monitoring ball, which specifically includes the following steps.

[0034] S1. Deploy the main intelligent monitoring sphere at the grid connection point and deploy the slave intelligent monitoring spheres at the output terminals of each distributed power source to form a star-shaped sensing network.

[0035] In some embodiments, both the master intelligent monitoring sphere and the slave intelligent monitoring sphere integrate an electrical quantity acquisition module, an environmental monitoring module, a video recognition module, a time synchronization module, and a wireless communication module, for synchronously acquiring power grid parameters, power supply operating status parameters, and environmental parameters.

[0036] The star-shaped sensing network based on smart monitoring spheres adopts a master-slave star topology. One master smart monitoring sphere is deployed at the grid connection point, and one slave smart monitoring sphere is deployed at the output end of each distributed power source (including photovoltaic inverters, wind turbines, energy storage converters, etc.). The master smart monitoring sphere is connected to the coordination and control center via optical fiber, and the slave smart monitoring spheres form a low-latency data transmission network with the master smart monitoring sphere via wireless communication (such as 5G). Each smart monitoring sphere is equipped with the following modules: (1) Electrical quantity acquisition module, which can use a 16-bit high-precision ADC sampling chip to synchronously acquire three-phase voltage and current waveforms at a frequency of 10kHz, and obtain the fundamental amplitude, phase and frequency parameters through FFT calculation; (2) Environmental monitoring module, which can integrate a light sensor (measurement range 0-1500W / m) 2 An anemometer (measuring range 0-30m / s) and a temperature sensor (-40℃~85℃) are used, with a sampling interval of 1 second; (3) Video recognition module, using a 2-megapixel industrial camera, combined with image processing algorithm, to identify the status of equipment indicator lights and abnormal mechanical parts in real time; (4) High-precision timing module with built-in GPS / BeiDou dual-mode receiver, which can provide μs-level time synchronization signal.

[0037] In some embodiments, a star-shaped sensing network is constructed using a microgrid comprising three photovoltaic arrays and two wind turbines as an example. Specifically, this includes: 1) installing a master smart monitoring sphere at the 380V grid-connected bus, equipped with an IP67 protective enclosure; 2) installing slave smart monitoring spheres at the AC output (270VAC) of each photovoltaic inverter; 3) the master smart monitoring sphere maintains time synchronization with the slave smart monitoring spheres via the PTP protocol, with an error of less than 50μs; 4) all monitoring spheres upload a data packet every 100ms, including: timestamp, three-phase voltage RMS value (in V), current RMS value (A), frequency (Hz), phase angle (°), and illuminance (W / m²). 2 ), wind speed (m / s) and other data from the same source; 5) when a sudden drop of 30% in the light intensity of a photovoltaic array is detected for 5 seconds, the video recognition module is triggered to take a close-up shot of the inverter, and the sampling frequency is increased to 20kHz for 10 seconds.

[0038] S2. The coordination and control center receives the same source data collected by the star-shaped sensing network, calculates the pre-synchronization target parameters and adjustment weights of each distributed power source based on the multi-objective optimization algorithm, generates coordination control commands, and allocates them to the virtual synchronous motor module.

[0039] In some embodiments, the coordination control center receives source data collected by the star-shaped sensing network, calculates the pre-synchronization target parameters and adjustment weights of each distributed power source based on a multi-objective optimization algorithm, and generates coordination control commands, including: the coordination control center receives source data collected by the star-shaped sensing network and performs filtering and weighted fusion processing to generate global data; based on the global data, it calculates the pre-synchronization target parameters and adjustment weights of each distributed power source through a multi-objective optimization algorithm and generates coordination control commands.

[0040] In some embodiments, the adjustment weight is a nonlinear function of the state of charge, light intensity, and wind speed of each distributed power source, in order to minimize grid-connected inrush current and avoid adjustment conflicts.

[0041] The coordination and control center adopts a hierarchical decision-making architecture, which includes the following key processing steps: 1. Data Preprocessing Layer The data processing unit receives source data from the star-shaped sensing network and performs three levels of data processing: (1) Time-domain filtering: A sliding window mid-range filtering algorithm is used to dynamically adjust the window width. The formula is as follows: ; in, Sampling frequency, The fundamental frequency, is the standard deviation of adjacent sampling points, and k is the sensitivity coefficient (ranging from 0.5 to 1.2). When a transient process is detected, it automatically switches to Kalman filtering.

[0042] (2) Frequency domain correction: Spectral leakage is compensated by windowed FFT, and frequency domain correction is performed using the Nuttall window function. The formula is as follows: ; in, Let N be the window function value for the nth sampling point, where N is the length of the Nuttall window function, i.e., the total number of sampling points involved in the windowing process. , , , These are all coefficients of the Nuttall window function. The value is 0.355768. The value is 0.487396. The value is 0.144232. The value is 0.012604.

[0043] (3) Multi-source data fusion: Establish an evidence theory model and define the basic probability allocation function, the formula of which is: ; Where x is the input value or observation value, , All are weighting factors. Let be the mean of the i-th Gaussian distribution. Let be the mean of the j-th Gaussian distribution. Let be the standard deviation of the i-th Gaussian distribution. Let be the standard deviation of the j-th Gaussian distribution, and n be the number of Gaussian distributions. Let be the confidence level for the i-th Gaussian distribution.

[0044] 2. Optimize the decision-making level The coordination decision-making unit uses the NSGA-III algorithm for multi-objective optimization, with the following formula: ; in, For inrush current, For frequency deviation, To adjust the weights, Let N be the power loss of the i-th node, and N be the number of nodes.

[0045] (2) Dynamic weight allocation, the formula is: ; Among them, the weighting coefficient Adaptive adjustment based on power supply type This is the weighting coefficient for photovoltaic power sources, with a value of 0.6. This is the weighting factor for wind power, with a value of 0.7. For energy storage power, the value is 0.8. Let i be the state of charge of the battery at the i-th node. The adjustment power of the i-th node, , These are other relevant parameters for the i-th and j-th nodes (such as power loss, voltage deviation, etc.).

[0046] S3. The virtual synchronous motor module receives the coordination control command and dynamically adjusts the virtual inertia and damping coefficient based on the inertia-damping adaptive adjustment algorithm. At the same time, it adjusts the output voltage, frequency and phase through the pre-synchronization execution unit to synchronize the output of each distributed power source with the grid parameters.

[0047] In some embodiments, the virtual inertia and damping coefficient are adjusted based on the pre-synchronization target parameters contained in the coordination control instructions issued by the coordination control center, and the virtual synchronous motor modules of each distributed power source achieve dual-layer adaptive adjustment based on the online fitting parameters of local frequency and power deviation.

[0048] In some embodiments, the inertial-damped adaptive adjustment algorithm involved in this application includes the following specific implementation steps: First, the virtual synchronous motor module receives the reference inertia, damping parameters, and weight correction coefficients issued by the coordination control center, completes initialization, and loads the pre-synchronization target frequency and power parameters. Then, based on the local and grid-side data collected by the smart monitoring ball, it calculates the frequency deviation, power deviation, and the overall grid power fluctuation coefficient. Subsequently, it matches the corresponding adjustment coefficients according to the power type (energy storage, photovoltaic, wind power), and completes the parameter coarse adjustment in combination with the weight correction coefficient. Then, it fits the parameter correction amount online based on the local frequency and power deviation, and superimposes it to obtain the final inertia and damping coefficient and limits the parameter range. After that, the parameters are sent to the adjustment unit, and the adjustment effect is verified by the smart monitoring ball. After grid connection, the parameters are further optimized according to the grid disturbance situation, and the adjustment results are fed back to the coordination control center to realize global-local closed-loop collaborative optimization, thereby balancing the grid response speed and system stability and effectively suppressing power oscillation.

[0049] In some embodiments, the virtual synchronous motor module dynamically adjusts the virtual inertia and damping coefficient, and simultaneously adjusts the voltage, frequency, and phase through the pre-synchronization execution unit. Combined with the real-time feedback data from the intelligent monitoring ball, the control center continuously corrects the instructions until the voltage, frequency, and phase parameters are matched to the standard (voltage error ≤2%, frequency error ≤0.1Hz, phase difference ≤1°).

[0050] Appendix Figure 2 The virtual synchronous motor (VSG) multi-loop control system shown is the core execution structure for achieving precise pre-synchronization control in this application. Its signal flow and coupling relationship present a hierarchical closed-loop architecture. From the outside in, the system comprises four core modules: a power loop, a virtual impedance loop, a voltage loop, and a current loop. The outermost power loop is the decision layer, encompassing active power-frequency droop control and reactive power-voltage droop control. It calculates reference frequency and reference voltage commands based on power deviation. This loop simulates the inertia and damping characteristics of a synchronous motor and is the key entry point for receiving external coordination commands. These reference commands then flow to the virtual impedance loop, which is coupled in series in the control link to correct the reference signal and optimize the output impedance characteristics. The corrected commands finally enter the voltage and current loops, which, as the execution layer, are responsible for achieving precise signal modulation and output, generating pulse width modulation (PWM) signals to drive the inverter. This hierarchical coupling ensures that upper-level decisions can achieve precise control of the inverter's output electrical quantities through the precise execution of the inner loops.

[0051] In some embodiments, the virtual synchronous motor (VSG) multi-loop control system plays a crucial role as the core algorithm of the pre-synchronization execution unit (corresponding to method step S3) in the specific implementation steps of this application. Its application logic is reflected in the dynamic adaptation of the power loop: the virtual synchronous motor module receives coordination control commands generated by the coordination control center based on a multi-objective optimization algorithm. These commands include pre-synchronization target parameters and the adjustment weights of each distributed power source. Subsequently, the inertia-damping adaptive adjustment unit in the power loop immediately responds to the commands, dynamically adjusting the virtual inertia and damping coefficient, thereby changing the VSG's response characteristics to frequency and voltage deviations. Through this command-driven dynamic parameter adjustment, the power loop forcibly guides the reference frequency and reference voltage commands towards the pre-synchronization target parameters. The inner virtual impedance loop, voltage loop, and current loop work together to accurately execute the adjusted commands, ultimately enabling the inverter's output voltage, frequency, and phase to achieve high-precision synchronization with grid parameters, thereby achieving the technical goal of minimizing grid-connected inrush current.

[0052] S4. After the synchronization target is met, the grid connection switches are closed sequentially according to the preset power response priority order to complete the grid connection action.

[0053] In some embodiments, the preset power response priority is: energy storage power supply takes precedence over photovoltaic power supply, and photovoltaic power supply takes precedence over wind power supply.

[0054] In some embodiments, the coordination and control center issues grid connection commands according to a preset power response priority order (energy storage → photovoltaic → wind power), and the switches are closed in sequence to achieve smooth grid connection.

[0055] In some embodiments, the system further includes: after the grid connection operation is completed, continuously collecting source data from the star sensing network, dynamically adjusting the adjustment weight and pre-synchronization target parameters based on closed-loop feedback to maintain system stability, and optimizing power allocation according to power supply operating status parameters (such as energy storage SOC).

[0056] In some embodiments, the working process is described using a photovoltaic-wind power-storage cluster grid connection as an example, specifically including: (1) System initialization: Each intelligent monitoring ball completes self-test and time synchronization, the coordination control center loads the power reference parameters, the pre-synchronization execution unit is on standby, and the grid connection switch is disconnected. (2) Global parameter monitoring: Each intelligent monitoring ball collects grid parameters and power supply operating status at the grid connection point in real time, transmits the data to the coordination control center and completes fusion processing. (3) Coordination decision and instruction issuance: The coordination control center calculates the pre-synchronization target parameters and adjustment weights of each distributed power supply, and issues coordination control instructions to each virtual synchronous motor module. (4) Pre-synchronization execution: The virtual synchronous motor module starts the inertia-damping adaptive adjustment algorithm to dynamically adjust the virtual inertia coefficient and damping coefficient; the pre-synchronization execution unit adjusts the voltage, frequency, and phase, and combined with the real-time feedback data of the intelligent monitoring ball, the coordination control center continuously corrects the instructions until the voltage, frequency, and phase parameters match the standard (voltage error ≤2%, frequency error ≤0.1Hz, phase difference ≤1°). (5) Grid connection execution: The coordination control center issues coordination control commands according to the preset power response priority order (energy storage → photovoltaic → wind power), and the switches are closed in sequence to achieve smooth grid connection. (6) Stable operation after grid connection: The intelligent monitoring ball continuously monitors the grid and power supply status. If grid disturbance occurs, the coordination control center dynamically adjusts the adjustment weight and pre-synchronization target parameters based on closed-loop feedback to maintain system stability; at the same time, it optimizes power allocation according to the power supply operating status (such as energy storage SOC).

[0057] In some embodiments, the method further includes: virtual synchronous motor grid-connected pre-synchronization control logic, which uses the grid voltage phase as a reference, corrects frequency deviation through active loop and voltage deviation through reactive loop, and includes four key links: deviation detection, compensation adjustment, synchronization determination, and grid-connected execution. It also marks high-precision synchronization determination thresholds of amplitude deviation ≤10%, frequency deviation ≤1%, and phase deviation ≤10°.

[0058] It should be noted that, firstly, the appendix Figure 3The synchronization judgment thresholds (such as amplitude deviation ≤10%, frequency deviation ≤1%, phase deviation ≤10°) are typically used as internal or phased judgment criteria for the pre-synchronization control logic of virtual synchronous motors. These thresholds represent the basic requirements of traditional grid-connected control or the rough conditions for triggering the next fine adjustment step in the process. They are mainly used in the control logic to determine whether the system is close to synchronization, thus ensuring that the control system can enter the fine adjustment phase. However, these synchronization judgment thresholds are relatively lenient and are not the high-precision adjustment target ultimately pursued by this invention. Secondly, the high-precision synchronization judgment thresholds (i.e., voltage error ≤2%, frequency error ≤0.1Hz, phase difference ≤1°) are the core technical objective of this invention and the ultimate judgment criterion for the coordination control center to continuously correct control commands. These high-precision synchronization judgment thresholds are far higher than the accuracy level of existing technologies. Only when the output parameters of each distributed power source reach this stringent standard will the coordination control center issue a grid connection command (corresponding to step S4). Its function is to achieve the beneficial effect of "pre-synchronization accuracy improvement" of the present invention, to ensure that the inrush current generated at the moment of grid connection is minimized (≤1.2 times the rated current), thereby effectively protecting the power electronic devices of the inverter and improving the stability of the system grid connection.

[0059] Example 2 This embodiment, based on Embodiment 1 above, provides a distributed power coordination pre-synchronization control system enhanced by an intelligent monitoring sphere. Please refer to [link to previous document]. Figure 4 The system is used to implement the steps of the distributed power supply coordination pre-synchronization control method enhanced by intelligent monitoring ball as described in Embodiment 1 above. The main implementation modules of the system are: intelligent monitoring ball module, coordination control center, virtual synchronous motor module and pre-synchronization execution unit.

[0060] In some embodiments, the intelligent monitoring sphere module is used to deploy a master intelligent monitoring sphere at the grid connection point and slave intelligent monitoring spheres at the output terminals of each distributed power source, forming a star-shaped sensing network. Further details regarding the deployment configuration and sensing range of the intelligent monitoring spheres at the distributed power source site can be found in [link to relevant documentation]. Figure 5 The multi-source sensing units (a is the electrical quantity acquisition module, b is the environmental monitoring module, and c is the video recognition module), communication interfaces, and connection relationships with the virtual synchronous motor module of the control sphere are labeled to demonstrate the on-site layout of the star-shaped sensing network.

[0061] In some embodiments, the coordination control center consists of a data processing unit, a coordination decision-making unit, and an instruction allocation unit. It is used to receive the same source data collected by the star-shaped sensing network, calculate the pre-synchronization target parameters and adjustment weights of each distributed power source based on a multi-objective optimization algorithm, generate coordination control instructions, allocate them to the virtual synchronous motor module, and, after synchronization is achieved, close the grid connection switch in sequence according to the preset power source response priority order to complete the grid connection action.

[0062] In some embodiments, the virtual synchronous motor module is deployed in each distributed power grid-connected inverter and has a built-in inertia-damping adaptive adjustment unit for receiving the coordinated control command and dynamically adjusting the virtual inertia and damping coefficient based on the inertia-damping adaptive adjustment algorithm.

[0063] In some embodiments, the pre-synchronization execution unit consists of a voltage regulation component, a frequency regulation component, and a phase regulation component, connected in series between the inverter and the grid-connected switch, and is used to regulate the output voltage, frequency, and phase so that the output of each distributed power source is synchronized with the grid parameters.

[0064] Example 3 Based on Embodiment 1 described above, this embodiment also provides an electronic device, please refer to the appendix. Figure 6 , Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0065] like Figure 6 As shown, an electronic device may include a processing unit (such as a central processing unit, graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from a storage device into random access memory (RAM). The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0066] Typically, the following devices can be connected to an I / O interface: input devices such as touchscreens, touchpads, keyboards, mice, and cameras; output devices such as liquid crystal displays (LCDs) and speakers; storage devices such as magnetic tapes and hard drives; and communication devices. Communication devices allow electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively. Figure 6 Each box shown can represent a device or multiple devices as needed.

[0067] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0068] Example 4 Based on Embodiment 1 above, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above method.

[0069] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), or any suitable combination thereof.

[0070] In this embodiment, the client and server can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol), and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0071] The aforementioned computer-readable medium may be included in the aforementioned device or may exist independently without being assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: deploy a main intelligent monitoring sphere at the grid connection point and secondary intelligent monitoring spheres at the output terminals of each distributed power source, forming a star-shaped sensing network; the coordination control center receives the same-source data collected by the star-shaped sensing network, calculates the pre-synchronization target parameters and adjustment weights of each distributed power source based on a multi-objective optimization algorithm, generates coordination control commands, and distributes them to the virtual synchronous motor module; the virtual synchronous motor module receives the coordination control commands, dynamically adjusts the virtual inertia and damping coefficient, and simultaneously adjusts the output voltage, frequency, and phase through the pre-synchronization execution unit to synchronize the output of each distributed power source with the grid parameters; after synchronization is achieved, the grid connection switches are closed sequentially according to the preset power source response priority order to complete the grid connection action.

[0072] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0074] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a smart monitoring ball module, a coordination control center, a virtual synchronous motor module, and a pre-synchronization execution unit. The names of these units do not necessarily limit the specific unit; for example, a smart monitoring ball module may also be described as "a unit for deploying a master smart monitoring ball at the grid connection point and slave smart monitoring balls at the output terminals of each distributed power source, forming a star-shaped sensing network."

[0075] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), etc.

[0076] Obviously, those skilled in the art will understand that the various steps of the present invention described above can be performed in a manner different from that described above, and the simulation methods and experimental equipment include, but are not limited to, the above description. The steps of the present invention described above can be performed in a different order in certain circumstances, and the steps shown or described above can be performed separately. Therefore, the present invention is not limited to any particular combination of hardware and software.

[0077] The above description, in conjunction with specific embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent monitoring ball enhanced distributed generation coordinated pre-synchronization control, characterized in that, The application relates to a grid-connected power supply system, which comprises the following steps: a master intelligent monitoring ball is arranged at a grid-connected point, and a slave intelligent monitoring ball is arranged at the output end of each distributed power supply, so as to form a star-shaped sensing network; a coordination control center receives homologous data collected by the star-shaped sensing network, calculates pre-synchronization target parameters and adjustment weights of each distributed power supply based on a multi-target optimization algorithm, generates a coordination control instruction, and distributes the coordination control instruction to a virtual synchronous motor module; the virtual synchronous motor module receives the coordination control instruction, dynamically adjusts virtual inertia and damping coefficients based on an inertia-damping adaptive adjustment algorithm, and adjusts output voltage, frequency and phase through a pre-synchronization execution unit, so that the output of each distributed power supply is synchronized with grid parameters; after synchronization is up to the standard, grid-connected switches are closed in turn according to a preset power supply response priority order, and grid-connected action is completed.

2. The method of claim 1, wherein the method is characterized by, The master intelligent monitoring ball and the slave intelligent monitoring ball are integrated with an electrical quantity collection module, an environment monitoring module, a video identification module, a time service module and a wireless communication module, and are used for synchronously collecting grid parameters, power supply operation state parameters and environment parameters.

3. The method of claim 1, wherein the method further comprises: The coordination control center receives homologous data collected by the star-shaped sensing network, calculates pre-synchronization target parameters and adjustment weights of each distributed power supply based on a multi-target optimization algorithm, generates a coordination control instruction, and distributes the coordination control instruction to a virtual synchronous motor module; The coordination control center receives homologous data collected by the star-shaped sensing network and carries out filtering and weighted fusion processing to generate global data; Based on the global data, a multi-target optimization algorithm is used to calculate pre-synchronization target parameters and adjustment weights of each distributed power supply, and a coordination control instruction is generated.

4. The method of claim 3, wherein the method further comprises: The adjustment weights are nonlinear functions of state of charge, light intensity and wind speed of each distributed power supply, so as to minimize grid-connected impact current and avoid adjustment conflicts.

5. The method of claim 1, wherein, The virtual inertia and damping coefficients are adjusted based on pre-synchronization target parameters contained in the coordination control instruction issued by the coordination control center, and the virtual synchronous motor module of each distributed power supply on-line fits parameters based on local frequency and power deviation, so that double-layer adaptive adjustment is realized.

6. The method of intelligent monitoring ball enhanced distributed generation coordinated pre-synchronization control, according to claim 1, wherein, The preset power supply response priority is that energy storage power supply is prior to photovoltaic power supply, and photovoltaic power supply is prior to wind power supply.

7. The method of claim 1, wherein, The application further comprises the following steps: After grid-connected action is completed, homologous data of the star-shaped sensing network are continuously collected, adjustment weights and pre-synchronization target parameters are dynamically adjusted based on closed-loop feedback, system stability is maintained, and power distribution is optimized according to power supply operation state parameters.

8. A smart monitoring ball enhanced distributed generation coordinated pre-synchronization control system, characterized in that, The application comprises the following steps: an intelligent monitoring ball module is used for arranging a master intelligent monitoring ball at a grid-connected point and arranging a slave intelligent monitoring ball at the output end of each distributed power supply, so as to form a star-shaped sensing network; a coordination control center is composed of a data processing unit, a coordination decision unit and an instruction distribution unit, is used for receiving homologous data collected by the star-shaped sensing network, calculating pre-synchronization target parameters and adjustment weights of each distributed power supply based on a multi-target optimization algorithm, generating a coordination control instruction, distributing the coordination control instruction to a virtual synchronous motor module, and closing grid-connected switches in turn according to a preset power supply response priority order after synchronization is up to the standard, and completing grid-connected action; The virtual synchronous motor module is deployed in each distributed power grid-connected inverter and has a built-in inertia-damping adaptive adjustment unit for receiving the coordinated control command and dynamically adjusting the virtual inertia and damping coefficient based on the inertia-damping adaptive adjustment algorithm. The pre-synchronization execution unit consists of a voltage regulation component, a frequency regulation component, and a phase regulation component. It is connected in series between the inverter and the grid-connected switch and is used to regulate the output voltage, frequency, and phase to synchronize the output of each distributed power source with the grid parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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