Multi-module parallel balance coordination control method, device, equipment and storage medium

Abstract

The invention relates to a multi-module parallel balance coordination control method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the time sequence decoupling analysis of a multi-module parallel network based on original state data, and obtaining a relative state feature set between modules; if the relative state feature sets are not in respective preset state ranges, performing load balancing coordination analysis on the multi-module parallel network based on the relative state feature sets through a Lagrange multiplier method to obtain a load balancing coordination distribution scheme; performing control instruction conversion on the load balancing coordination distribution scheme to obtain a control instruction sequence of each module; and analyzing the control instruction sequence and implementing the control instruction sequence to each module so as to realize load balancing of the multi-module parallel network, thereby solving the technical problem that the condition of uneven load distribution is aggravated due to the fact that a traditional method may not timely respond when the power grid condition fluctuates or the load suddenly changes.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-module parallel network, and in particular to a multi-module parallel balanced coordination control method, device, equipment and storage medium. Background Art

[0002] Multi-module parallel networks play a vital role in modern power electronic systems, especially in applications that require high power and high reliability. With the development of technology, more and more power electronic devices have adopted modular design, and large-capacity output and redundant configuration are achieved by operating multiple identical or different power modules in parallel. However, this architecture brings about the problem of load distribution: due to factors such as manufacturing tolerances, environmental changes, and aging effects, performance differences between modules are inevitable, which may cause some modules to bear too much load and fail prematurely, thus affecting the stability and reliability of the entire system. Therefore, how to ensure that these parallel modules can share the load evenly has become a research focus.

[0003] Traditional load balancing methods often rely on static settings or simple feedback control, but such methods are difficult to adapt to complex dynamic conditions and instantaneous changes. In actual operation, when grid conditions fluctuate or load changes suddenly, traditional methods may not respond in time, resulting in an increase in uneven load distribution. In addition, the lack of in-depth understanding of the interaction between multiple modules also limits the effectiveness of existing technologies. This requires the development of a more intelligent and efficient load balancing coordination control strategy that can monitor and adjust the working status of each module in real time to ensure that good load distribution can be maintained even under non-ideal conditions, while improving the overall efficiency and life of the system.

[0004] In order to solve the above problems, a new load balancing coordination control method based on a multi-module parallel network is proposed. This method first uses a high-speed synchronous sampler to obtain the real-time operating parameters of each module in the network, and then performs a timing decoupling analysis to extract a data set reflecting the relative state characteristics between modules. If it is found that these characteristics deviate from the preset safety range, the Lagrange multiplier method is introduced to perform load balancing coordination analysis, and an optimized load distribution plan is formulated and converted into a specific control instruction sequence for each module. This method not only takes into account the dynamic interaction characteristics between different modules, but also provides a fast and accurate response mechanism, which is of great significance for improving the performance of multi-module parallel systems. Summary of the invention

[0005] The main purpose of the present invention is to provide a multi-module parallel balanced coordinated control method, device, equipment and storage medium, which solves the technical problem that when the power grid conditions fluctuate or the load suddenly changes, the traditional method may not respond in time, resulting in aggravated uneven load distribution.

[0006] To achieve the above object, the present invention provides a multi-module parallel balanced coordinated control method, which is applied to a multi-module parallel network, wherein the multi-module parallel network includes a plurality of modules connected to each other, and comprises the following steps: The operating parameters of the multi-module parallel network are synchronously collected by a preset high-speed synchronous sampler to obtain original state data; Based on the original state data, a timing decoupling analysis is performed on the multi-module parallel network to obtain a relative state feature set between modules; If the relative state feature set is not within the respective preset state range, a load balancing coordination analysis is performed on the multi-module parallel network based on the relative state feature set by Lagrange multiplier method to obtain a load balancing coordination allocation scheme; The load balancing coordination and allocation scheme is converted into control instructions to obtain a control instruction sequence for each module; The control instruction sequence is parsed and implemented in each module to achieve load balancing of the multi-module parallel network.

[0007] Furthermore, the operating parameters of the multi-module parallel network are synchronously collected by a preset high-speed synchronous sampler to obtain original state data, including: The voltage and current signals of each module are sampled by a high-speed synchronous sampler to obtain an original sampling sequence; Performing phase compensation processing on the original sampling sequence based on a time synchronization trigger to obtain a synchronous sampling data stream; Performing multi-scale decomposition on the synchronous sampling data stream by a wavelet packet decomposer to obtain multi-scale feature data; The multi-scale feature data is subjected to spectrum analysis based on discrete Fourier transform to obtain original state data; wherein the original state data includes fundamental wave parameters, harmonic content and phase sequence relationship of each module, and the phase sequence relationship is the phase sequence relationship between the three-phase voltage and the three-phase current inside each module.

[0008] Furthermore, the timing decoupling analysis of the multi-module parallel network is performed based on the original state data to obtain a relative state feature set between modules, including: Performing frequency domain transformation on the original state data through a preset frequency domain decomposer to obtain a frequency domain feature vector, and performing principal component analysis on the frequency domain feature vector to obtain a frequency domain principal component set; Performing Hilbert transform on the frequency domain principal component set to obtain an instantaneous frequency sequence; Performing state estimation on the instantaneous frequency sequence by using Kalman filtering to obtain an estimated state quantity, and performing state space model analysis on the estimated state quantity to obtain a state transfer matrix; Based on the state transfer matrix, the multi-module parallel network is time-sequentially decoupled to obtain a decoupled state quantity, and a covariance analysis is performed on the decoupled state quantity to obtain a dynamic coupling relationship between modules; The dynamic coupling relationship between the modules is classified by fuzzy clustering analysis to obtain clustering results, and the clustering results are subjected to inter-class similarity analysis to obtain a relative state feature set between the modules; wherein the relative state feature set includes power state characteristics, electrical state characteristics and dynamic state response.

[0009] Furthermore, the load balancing coordination analysis of the multi-module parallel network is performed based on the relative state feature set by the Lagrange multiplier method to obtain a load balancing coordination allocation scheme, including: Performing adaptive weight allocation on the relative state feature set to obtain a weighted feature matrix, and performing singular spectrum analysis on the weighted feature matrix to obtain a feature spectrum component set; Constructing constraint conditions for the characteristic spectrum component set through nonlinear programming to obtain an optimization objective function, and performing Lagrangian multiplier expansion on the optimization objective function to obtain a Lagrangian function set, wherein the Lagrangian function set includes a power balance constraint, a voltage stability constraint, and a frequency synchronization constraint; Iteratively solving the Lagrangian function set based on the Lagrangian multiplier method to obtain an optimal solution sequence; Based on the optimal solution sequence, a load balancing coordination analysis is performed on the multi-module parallel network to obtain a load balancing coordination allocation scheme; wherein the load balancing coordination allocation scheme includes an optimal power allocation scheme, an optimal voltage regulation scheme and an optimal frequency compensation scheme.

[0010] Furthermore, the optimization objective function is subjected to Lagrangian multiplier expansion to obtain a Lagrangian function set, including: Performing a dual transformation on the optimization objective function to obtain a dual constraint function group, and performing gradient projection on the dual constraint function group to obtain a constraint gradient field; Performing saddle point analysis on the constrained gradient field to obtain a saddle point solution sequence; wherein the saddle point solution sequence includes a power saddle point solution, a voltage saddle point solution and a frequency saddle point solution; Based on Wolfe duality theory, the saddle point solution sequence is Lagrangian expanded to obtain a set of Lagrangian functions.

[0011] Furthermore, the load balancing coordination allocation scheme is converted into control instructions to obtain a control instruction sequence for each module, including: Performing pulse width modulation conversion on the load balancing coordination allocation scheme by space vector decomposition to obtain a switching timing sequence; Dynamically compensating the switch timing sequence based on a state feedback matrix to obtain a compensation control amount, and performing discrete quantization processing on the compensation control amount to obtain a digital control word; Performing feedforward prediction on the digital control word based on a preset prediction controller to obtain a prediction control sequence; Decoupling the prediction control sequence in parallel through a module decoupler to obtain an independent control quantity, and coordinating the independent control quantity in time sequence to obtain a synchronous control sequence; The synchronous control sequence is hardware mapped based on the instruction generator to obtain the control instruction sequence of each module.

[0012] Furthermore, the control instruction sequence is parsed and implemented in each module to achieve load balancing of the multi-module parallel network, including: Performing real-time slicing processing on the control instruction sequence to obtain a time-slicing instruction set, and performing parallel scheduling analysis on the time-slicing instruction set to obtain a module execution queue; Prioritizing the module execution queue to obtain a priority execution sequence, and performing deadlock detection and elimination on the priority execution sequence to obtain a deadlock-free execution sequence; Distributing instructions for the deadlock-free execution sequence based on a preset distributed message bus to obtain a module control message, and generating a CRC check code for the module control message to obtain a reliable transmission frame; And the reliable transmission frame is converted into a state to obtain an execution state sequence, The execution state sequence is implemented in each module to achieve load balancing of the multi-module parallel network.

[0013] The present invention also provides a multi-module parallel balanced coordination control device, which is applied to a multi-module parallel network. The multi-module parallel network includes a plurality of modules connected to each other, including: An acquisition module, used for synchronously acquiring the operating parameters of the multi-module parallel network through a high-speed synchronous sampler to obtain original state data; A first analysis module, configured to perform a timing decoupling analysis on the multi-module parallel network based on the original state data to obtain a relative state feature set between modules; A second analysis module is used to perform load balancing coordination analysis on the multi-module parallel network based on the relative state feature set by Lagrange multiplier method to obtain a load balancing coordination allocation scheme if the relative state feature set is not within the respective preset state range; A conversion module, used to convert the load balancing coordination allocation scheme into control instructions to obtain a control instruction sequence for each module; The implementation module is used to parse the control instruction sequence and implement it to each module to achieve load balancing of the multi-module parallel network.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The multi-module parallel balanced coordinated control method provided by the present invention comprises the following steps: synchronously collecting the operating parameters of the multi-module parallel network through a high-speed synchronous sampler to obtain original state data; performing time decoupling analysis on the multi-module parallel network based on the original state data to obtain a relative state feature set between modules; if the relative state feature set is not within the respective preset state ranges, performing load balancing coordinated analysis on the multi-module parallel network based on the relative state feature set through the Lagrange multiplier method to obtain a load balancing coordinated allocation scheme; performing control instruction conversion on the load balancing coordinated allocation scheme to obtain a control instruction sequence of each module; parsing the control instruction sequence and implementing it in each module to achieve load balancing of the multi-module parallel network, solving the technical problem that when the power grid conditions fluctuate or the load suddenly changes, the traditional method may not be able to respond in time, resulting in an aggravation of the uneven load distribution, and realizing that by converting the load balancing coordinated allocation scheme into a specific control instruction sequence and parsing and implementing it in each module, the method realizes the refined management of the working state of each module, maximizes the potential of each module, improves resource utilization, and also reduces the maintenance cost and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of a multi-module parallel balanced coordinated control method in one embodiment of the present invention; Figure 2 It is a structural block diagram of a multi-module parallel balanced coordination control device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a multi-module parallel balanced coordinated control method in one embodiment of the present invention; In one embodiment of the present invention, a multi-module parallel balanced coordinated control method is provided, which is applied to a multi-module parallel network, wherein the multi-module parallel network includes a plurality of modules connected to each other, and includes the following steps: Step S1, synchronously collect the operating parameters of the multi-module parallel network through a preset high-speed synchronous sampler to obtain original state data.

[0021] Specifically, in the process of implementing the multi-module parallel balanced coordinated control method, it is crucial to synchronously collect the operating parameters of the multi-module parallel network through a high-speed synchronous sampler to obtain the original state data. In order to ensure that the load distribution between the modules can achieve the ideal balanced effect, it is necessary to first accurately understand the current working state of each module. The "high-speed synchronous sampler" mentioned here is a high-precision measurement tool specially designed for power electronic systems. It can read data from multiple points at the same time in a very short time interval, ensuring that the data collection between different modules is strictly synchronized. For example, in a distributed power generation system composed of multi-module parallel inverters, these inverters may be connected to a common AC bus and transmit power to the power grid. Due to factors such as ambient temperature changes and load fluctuations, the actual output power of each inverter may be different. In order to maintain the stable operation of the system and optimize efficiency, we need to grasp the state of each inverter in real time. At this time, the high-speed synchronous sampler comes into play. It can sample the key parameters of each inverter such as voltage and current at a speed of microseconds or even faster. These parameters constitute the so-called "operating parameters". When the sampler completes the simultaneous acquisition of all relevant parameters, the data set obtained is the "raw state data". These data not only contain the specific working conditions of each module at each moment, but also because they are the results of synchronous acquisition, they are directly comparable, which provides a solid foundation for subsequent timing decoupling analysis. For example, if we find that the current value of a specific inverter increases abnormally during a certain period of time, we can determine whether there is a problem and the root cause of the problem by comparing the data of other inverters at the same time point. In this way, based on the raw state data, we can accurately identify which modules are in non-ideal working conditions, and then take measures to adjust their loads to achieve load balancing of the entire multi-module parallel network. Therefore, this step plays a foundational role in the entire control method, ensuring that all subsequent operations can be based on an accurate and synchronized data set.

[0022] Step S2, performing a timing decoupling analysis on the multi-module parallel network based on the original state data to obtain a relative state feature set between modules.

[0023] Specifically, after realizing the synchronous acquisition of the operating parameters of the multi-module parallel network and obtaining the original state data, the next key step is to perform time-series decoupling analysis on the multi-module parallel network based on these original state data to obtain the relative state feature set between modules. The core of this process is to deeply analyze the behavior pattern of each module over time and identify the mutual influence between them, so as to provide a basis for subsequent load balancing coordination. For example, in a distributed power generation system composed of multi-module parallel inverters, we have obtained the key parameters such as voltage and current of each inverter at different time points through a high-speed synchronous sampler. Now, we need to further understand the dynamic relationship behind these data. Time-series decoupling analysis is a method designed for this purpose. It decomposes the originally complex multivariate time series into a form that is easier to handle and understand. Specifically, it is to extract the independent time evolution characteristics of each module from the original state data, and at the same time reveal the potential connection between modules. Through in-depth mining of these data, we can find that some inverters may show consistent change trends under certain conditions, while other inverters show different response patterns. Through this analysis, we can construct a data set reflecting the relative state characteristics between modules, namely the "relative state feature set". This feature set not only describes the state of a single module, but more importantly, it shows how the modules interact with each other and how they differ from each other. For example, when grid conditions change, some inverters may quickly adjust their output power to adapt to new demands, while others may react more slowly or exhibit different adjustment strategies. By comparing these relative state features, we can more accurately identify which modules are in the best working state, which modules need to be adjusted, and even predict possible problems in the future. Therefore, this analysis step lays the foundation for formulating an effective load balancing coordination scheme, allowing us to flexibly adjust the workload of each module according to the actual working conditions to ensure the stability and efficient operation of the entire system. In summary, the timing decoupling analysis based on the original state data is a complex but crucial link, which helps us clarify the operating details of the multi-module parallel network and extract valuable information to guide subsequent optimization measures. This not only improves the overall performance of the system, but also provides strong support for the realization of intelligent power management.

[0024] Step S3: If the relative state feature set is not within the respective preset state range, a load balancing coordination analysis is performed on the multi-module parallel network based on the relative state feature set by Lagrange multiplier method to obtain a load balancing coordination allocation plan.

[0025] Specifically, after completing the timing decoupling analysis of the multi-module parallel network and obtaining the relative state feature set between the modules, it is necessary to evaluate whether these features fall within the preset state range. If it is found that the relative state feature set is not within the respective preset state range, it means that the current load distribution may not achieve the ideal equilibrium state, and some modules may be overloaded or underloaded. At this time, in order to optimize the system performance, further measures need to be taken to adjust the workload of each module. Specifically, the load balancing coordination analysis of the multi-module parallel network based on the relative state feature set by the Lagrange multiplier method is an important means to achieve this goal. The Lagrange multiplier method is a mathematical method for solving optimization problems with constraints. Here it is used to deal with complex and interrelated load distribution problems in multi-module parallel networks. This method can find an optimal load distribution scheme while keeping the total output of the system unchanged, so that the working state of each module is as close to its optimal working point as possible, while satisfying the overall constraints of the system, such as power balance, voltage stability, etc. For example, in a distributed power generation system consisting of multiple parallel inverters, suppose we have identified that the current values ​​of some inverters are beyond the normal range, which indicates that they are bearing too much or too little load. At this time, we can use the Lagrange multiplier method to build an optimization model, taking the current load of each inverter as a variable, and minimizing the load difference between all inverters as the objective function. At the same time, considering the needs of the power grid and the requirements for safe operation, we fix the total power output as a constant and ensure that the voltage level is maintained within an acceptable range. By solving this optimization problem, we can obtain a new set of load distribution recommendations, namely the "load balancing coordination allocation scheme". According to the obtained load balancing coordination allocation scheme, we can know which inverters need to increase or decrease their output power, and the specific adjustment range. In this way, even in the face of complex dynamic working conditions, we can respond quickly and adjust the load of each module in time to avoid the risk of failure caused by local overload, while improving the efficiency and stability of the entire system. Therefore, this step not only solves the problem of uneven load distribution, but also provides a scientific basis for future continuous optimization, ensuring that the multi-module parallel network can always operate efficiently and reliably.

[0026] Step S4, converting the load balancing coordination allocation scheme into control instructions to obtain a control instruction sequence for each module.

[0027] Specifically, after the formulation of the load balancing coordination allocation scheme is completed, the next key step is to convert the load balancing coordination allocation scheme into control instructions to obtain the control instruction sequence of each module. This process is to ensure that the optimized load distribution strategy can be actually applied to the multi-module parallel network, so that each module adjusts its working state in a predetermined manner, thereby achieving an ideal load balancing effect. Specifically, in a distributed power generation system composed of multi-module parallel inverters, we have obtained a scheme aimed at optimizing the load distribution of each inverter through the Lagrange multiplier method. However, this scheme exists in the form of theoretical numerical values ​​and cannot be directly used to control hardware devices. Therefore, we need to further process these numerical values ​​and convert them into specific control commands, namely "control instruction sequences". This step involves mapping each decision point in the load balancing coordination allocation scheme into specific operation instructions that the corresponding module can understand and execute. For example, if the optimization scheme indicates that a certain inverter needs to reduce the output power to avoid overload, then the corresponding control instruction may be to adjust the PWM (pulse width modulation) duty cycle of the inverter or change its reference voltage setting value. In order to achieve this conversion, a set of predefined rules or algorithms are usually used. These rules and algorithms can understand the results of the optimization model output and generate control signals suitable for a specific hardware platform. In this process, factors such as the communication protocol, response time and control accuracy between different inverters must be taken into account to ensure that all instructions can be accurately transmitted and executed. In addition, a mechanism needs to be designed to handle possible abnormal situations. For example, when an inverter fails to receive or execute instructions correctly, the system should have the ability to self-diagnose and repair to ensure the stable operation of the entire system. For example, in the distributed power generation system mentioned above, suppose that after timing decoupling analysis and Lagrange multiplier optimization, we determine that the output power of some inverters needs to be adjusted. At this time, we convert these adjustment requirements into a series of precise control instructions, such as increasing or decreasing current, adjusting frequency, etc., and then send them to the corresponding inverters through appropriate communication interfaces. After each inverter receives its own control instruction sequence, it will adjust its own operating parameters according to these instructions, thereby achieving the purpose of overall load balancing. In this way, not only an effective transition from theory to practice is achieved, but also the entire multi-module parallel network can maintain an efficient and reliable working state under complex working conditions. In summary, the process of converting the load balancing coordination allocation scheme into control instructions is an important bridge connecting theory and practice. It ensures that the optimization results can effectively affect the actual operation of the system, so that each module can adjust its own behavior according to the optimal solution, and finally achieve the best performance of the entire multi-module parallel network.

[0028] Step S5, parsing the control instruction sequence and implementing it in each module to achieve load balancing of the multi-module parallel network.

[0029] Specifically, in the process of parsing the control instruction sequence and implementing it into each module to achieve load balancing of the multi-module parallel network, the key is to ensure that each control instruction can be accurately understood and effectively executed. This process directly determines whether the optimized load distribution scheme can be correctly applied in the actual system, so as to achieve the expected performance improvement effect. Specifically, in a distributed power generation system composed of multi-module parallel inverters, we have obtained a specific control instruction sequence for each inverter through a series of steps. These instruction sequences include operations such as adjusting output power and changing operating frequency, but they exist in an abstract form and need further parsing to become actual signals that the inverter can recognize and respond to. In order to accomplish this task, a specially designed parsing unit or software module is usually used, which is responsible for interpreting the control instructions converted from the load balancing coordination distribution scheme and converting them into a format that conforms to the communication protocol of each inverter. For example, if an inverter uses the Modbus RTU communication protocol, the parsing unit will convert the general control instructions into a series of command frames under the protocol so that the inverter can correctly receive and process them. Once the parsing is completed, the next step is the implementation stage. This involves not only sending the parsed instructions to the corresponding inverters, but also monitoring the response of each inverter to the instructions to ensure that they adjust their working status as expected. In this process, various challenges may be encountered, such as communication delays, data transmission errors, or hardware failures of individual inverters. To cope with these problems, the system should have real-time feedback mechanisms and self-correction capabilities. For example, when an inverter fails to respond correctly to the control instructions it receives, the system can automatically resend the instructions or take other remedial measures to ensure the stability and reliability of the entire system. For example, in the distributed power generation system mentioned above, assume that we have generated a set of control instruction sequences based on the optimization results, instructing some inverters to increase output power while others need to reduce it. The parsing unit will first convert these instructions into data packets suitable for the communication protocol of each inverter, and then send these data packets through a dedicated communication line. After each inverter receives its own control instruction, it will immediately start to adjust its operating parameters, such as PWM duty cycle or reference voltage set value, to adapt to the new load requirements. At the same time, the system will continue to monitor the status of each inverter to ensure that they are all properly adjusted according to the predetermined plan. If any abnormality is found, the system will take corrective measures in time to ensure that all inverters can eventually achieve the ideal load balancing state. In summary, parsing the control instruction sequence and implementing it in each module is the last and most important link in achieving load balancing in a multi-module parallel network.It not only ensures that the optimization strategy can take root in the actual environment, but also provides the necessary guarantee mechanism, so that even in the face of complex and changing working conditions, the entire system can maintain efficient and stable operation. Through this process, we can truly transform the theoretical optimization results into actual performance improvement, providing strong support for the application and development of multi-module parallel systems.

[0030] In a specific embodiment, the operation parameters of the multi-module parallel network are synchronously collected by a preset high-speed synchronous sampler to obtain original state data, including: The voltage and current signals of each module are sampled by a high-speed synchronous sampler to obtain an original sampling sequence; Performing phase compensation processing on the original sampling sequence based on a time synchronization trigger to obtain a synchronous sampling data stream; Performing multi-scale decomposition on the synchronous sampling data stream by a wavelet packet decomposer to obtain multi-scale feature data; The multi-scale feature data is subjected to spectrum analysis based on discrete Fourier transform to obtain original state data; wherein the original state data includes fundamental wave parameters, harmonic content and phase sequence relationship of each module, and the phase sequence relationship is the phase sequence relationship between the three-phase voltage and the three-phase current inside each module.

[0031] Specifically, in a multi-module parallel network, in order to achieve efficient load balancing and coordinated control, it is first necessary to accurately and synchronously collect the operating parameters of each module. This process is completed by a high-speed synchronous sampler, which can not only quickly obtain the voltage and current signals of each module, but also ensure the time consistency of these data, thereby providing a solid foundation for subsequent analysis. Specifically, the process of synchronously collecting the operating parameters of the multi-module parallel network by a high-speed synchronous sampler to obtain the original state data includes multiple closely connected steps. First, the high-speed synchronous sampler samples the voltage and current signals of each module to obtain the original sampling sequence. At this stage, the sampler simultaneously reads the three-phase voltage and three-phase current values ​​at the output end of each inverter at an extremely high frequency (for example, tens of thousands of times per second). This is because in a distributed power generation system composed of multi-module parallel inverters, the working state of each inverter may be affected by multiple factors, such as grid conditions, load changes, etc., and these influences will eventually be reflected in the changes in voltage and current. Therefore, accurately capturing these dynamic changes is crucial to understanding the operating status of the entire system. The original sampling sequence obtained in this way contains a lot of information about the real-time working status of the inverter, but they have not been processed, and direct use may cause difficulties in data analysis. Next, the original sampling sequence is phase compensated based on the time synchronization trigger to obtain a synchronized sampling data stream. Since there may be slight time differences between the inverters, these differences will affect the subsequent data analysis results if they are not corrected. The role of the time synchronization trigger is to eliminate this time deviation so that the data from different inverters can be compared on the same time basis. For example, in a distributed power generation system composed of multiple inverters, even if there are slight differences in the internal clock of each inverter, through the processing of the time synchronization trigger, we can still ensure that the data of all inverters are collected synchronously, which provides a prerequisite for subsequent spectrum analysis and other forms of data mining. The synchronized sampling data stream not only retains all the information of the original sampling sequence, but also enhances the consistency and comparability of data between different modules, which is particularly important for identifying and solving load imbalance problems. Subsequently, the synchronized sampling data stream is multi-scale decomposed by a wavelet packet decomposer to obtain multi-scale feature data. Wavelet packet decomposition is a powerful signal processing technique that can decompose complex electrical signals into multiple sub-bands according to different frequency ranges, thereby revealing the various frequency components in the signal and their characteristics of time variation. In this case, the application of the wavelet packet decomposer means that we split the original continuous synchronous sampling data stream into a series of multi-scale feature data with different resolutions. Doing so can not only observe the transient behavior of each inverter in more detail, but also make it easier to identify useful information hidden in high-frequency noise.For example, when we are concerned about whether an inverter has abnormal vibration or overheating, multi-scale feature data can help us filter out specific frequency components related to these problems from a large amount of raw data, thereby providing a basis for fault diagnosis. Finally, the multi-scale feature data is spectrally analyzed based on discrete Fourier transform to obtain the original state data. Discrete Fourier transform (DFT) is a classic algorithm for converting time domain signals into frequency domain representations, which can help us determine the fundamental parameters, harmonic content, and phase sequence relationship in the signal. The "original state data" here specifically refers to the data set containing the above key indicators, which reflect the interaction between the three-phase voltage and the three-phase current in each module. Specifically, the fundamental parameter refers to the main frequency component of 50Hz or 60Hz (depending on the national grid standard); the harmonic content covers all integer multiples of the fundamental frequency; the phase sequence relationship describes the relative angle difference between the three-phase voltage and the three-phase current. Through in-depth analysis of these data, we can fully understand the working status of each inverter, find potential problems, and formulate reasonable optimization strategies accordingly. For example, in a distributed power generation system composed of multi-module parallel inverters, assume that we have completed all the above steps and obtained a complete set of raw state data. These data show that the three-phase current of one of the inverters has obvious imbalance, that is, the current of some phases is significantly higher than that of other phases. By further analyzing the fundamental wave parameters and phase sequence relationship of the inverter, we can confirm whether it has internal faults or is affected by external factors. If it is found that the temporary imbalance is caused by grid fluctuations, the output of other inverters can be adjusted to compensate for this problem; but if it is caused by the hardware failure of the inverter itself, maintenance measures need to be taken in time. In this way, we can make full use of the information provided by the raw state data to ensure the stable operation and efficient performance of the entire system. In summary, the process of synchronously collecting the operating parameters of the multi-module parallel network through a high-speed synchronous sampler to obtain the raw state data is a complex and delicate task. It involves knowledge and technology in multiple professional fields, including high-precision sampling, time synchronization, multi-scale signal processing, and spectrum analysis. Each link plays an indispensable role and together constitutes a complete and effective data acquisition and analysis framework. This framework not only provides us with an opportunity to gain a deeper understanding of the operating mechanism of multi-module parallel systems, but also lays a solid foundation for achieving accurate load balancing and coordinated control. In this way, we can ensure that the distributed generation system is always in the best working state to meet the growing power demand, while also providing strong technical support for the construction of future smart grids.

[0032] In a specific embodiment, the timing decoupling analysis of the multi-module parallel network is performed based on the original state data to obtain a relative state feature set between modules, including: Performing frequency domain transformation on the original state data through a preset frequency domain decomposer to obtain a frequency domain feature vector, and performing principal component analysis on the frequency domain feature vector to obtain a frequency domain principal component set; Performing Hilbert transform on the frequency domain principal component set to obtain an instantaneous frequency sequence; Performing state estimation on the instantaneous frequency sequence by using Kalman filtering to obtain an estimated state quantity, and performing state space model analysis on the estimated state quantity to obtain a state transfer matrix; Based on the state transfer matrix, the multi-module parallel network is time-sequentially decoupled to obtain a decoupled state quantity, and a covariance analysis is performed on the decoupled state quantity to obtain a dynamic coupling relationship between modules; The dynamic coupling relationship between the modules is classified by fuzzy clustering analysis to obtain clustering results, and the clustering results are subjected to inter-class similarity analysis to obtain a relative state feature set between the modules; wherein the relative state feature set includes power state characteristics, electrical state characteristics and dynamic state response.

[0033] Specifically, in order to deeply understand the dynamic behavior and interaction between each module in the multi-module parallel network, the process of performing time series decoupling analysis on the multi-module parallel network based on the original state data is an indispensable part. This process can not only reveal the operating mechanism hidden behind the complex power electronic system, but also provide a key basis for achieving efficient load balancing and coordinated control. Specifically, this step includes a series of closely connected technical processing, from frequency domain transformation to principal component analysis, to Hilbert transform, Kalman filtering, state space model analysis, and finally obtain the relative state feature set between modules through fuzzy clustering analysis. First, the original state data is transformed into the frequency domain by the frequency domain decomposer to obtain the frequency domain feature vector, and the frequency domain feature vector is subjected to principal component analysis to obtain the frequency domain principal component set. At this stage, we use the frequency domain decomposer to convert the time series information in the original state data into a frequency domain representation. For example, in a distributed power generation system composed of multi-module parallel inverters, the three-phase voltage and current signals of each inverter contain rich frequency information, which reflects the power flow under different frequency components. By performing fast Fourier transform (FFT) or discrete Fourier transform (DFT) on these signals, we can decompose them into a series of sinusoidal components of different frequencies, the so-called "frequency domain feature vectors". Subsequently, we apply principal component analysis (PCA) technology to screen out the main frequency components that best represent the overall characteristics of the system to form a "frequency domain principal component set". This step helps to reduce the amount of data for subsequent analysis while retaining the most important information, allowing us to focus more on those factors that have the greatest impact on system performance. Next, the frequency domain principal component set is Hilbert transformed to obtain an instantaneous frequency sequence. The Hilbert transform is a mathematical tool used to calculate the instantaneous amplitude and phase of a signal. Here it is used to further analyze the time evolution characteristics of the frequency domain principal component set. Through the Hilbert transform, we can obtain the instantaneous frequency sequence of each main frequency component over time, which provides us with a more detailed understanding of the working status of each inverter. For example, if the fundamental frequency of an inverter fluctuates, this change will be directly reflected in its instantaneous frequency sequence. Such information is crucial for identifying the transient response and potential problems of the system because it can help us discover phenomena that are difficult to detect with static analysis alone. Then, the Kalman filter is used to perform state estimation on the instantaneous frequency sequence to obtain the estimated state quantity, and the state space model analysis is performed on the estimated state quantity to obtain the state transfer matrix. Kalman filtering is a powerful recursive algorithm suitable for optimal estimation of time series data containing noise. In this process, we regard the instantaneous frequency sequence as part of the system output and assume that there is an implicit state variable sequence behind it, which determines the change of the actual observed value.Through Kalman filtering, we can extract the most likely state trajectory, i.e., the "estimated state quantity", from the observed data with random errors. Next, based on these estimated state quantities, we construct a state space model, which is a mathematical framework that describes the dynamic behavior of the system. By analyzing the state transfer matrix, we can understand how the various state variables affect each other and their evolution laws over time. This is of great significance for predicting future system behavior and formulating corresponding control strategies. Next, the multi-module parallel network is time-series decoupled based on the state transfer matrix to obtain the decoupled state quantity, and the decoupled state quantity is subjected to covariance analysis to obtain the dynamic coupling relationship between modules. The so-called "time series decoupling" refers to decomposing the originally complex multivariate time series into several independent or weakly correlated subsequences in order to better understand and process them. In this example, we reveal the intrinsic connections between the inverters through the state transfer matrix, and then use appropriate mathematical methods to separate these connections as much as possible, thereby obtaining a set of decoupled state quantities. After that, we perform covariance analysis on these decoupled state quantities to quantify the dynamic coupling strength between different modules. For example, if some state quantities of two inverters show a high correlation, then this may mean that they are physically related in some way, such as sharing the same grid connection point or being affected by similar external interference sources. In this way, we can see more clearly how the modules interact with each other, which provides a basis for subsequent optimization adjustments. Finally, the dynamic coupling relationship between the modules is classified by fuzzy clustering analysis to obtain clustering results, and the clustering results are analyzed for inter-class similarity to obtain the inter-module relative state feature set. The fuzzy clustering analysis here is an unsupervised learning method that allows objects to belong to multiple categories and is classified according to the membership degree of the members. By fuzzy clustering the dynamic coupling relationship between modules, we can classify modules with similar behavior patterns into one category while retaining the subtle differences between them. Subsequently, we perform inter-class similarity analysis on these clustering results in order to find out the common features and differences between different categories. The final "inter-module relative state feature set" covers multiple aspects such as power state characteristics, electrical state characteristics, and dynamic state response, and fully reflects the working conditions of each module and their relationship with each other. For example, in a distributed power generation system consisting of multiple inverters, if we find that the power output of one type of inverter is relatively stable, while the other type shows greater volatility, this may be due to the different working environments or task assignments they are in. Through such analysis, we can take targeted measures to optimize the working conditions of each module and ensure the efficient operation of the entire system. In summary, the process of performing timing decoupling analysis on the multi-module parallel network based on the original state data is a multi-level, multi-dimensional data mining and modeling activity.It combines advanced technologies such as frequency domain transformation, principal component analysis, Hilbert transform, Kalman filtering, state space model analysis, and fuzzy clustering to deeply analyze the complex dynamic behavior of multi-module parallel systems. Through this series of carefully designed steps, we can not only accurately capture the current working status of each module, but also predict their future development trends, thereby providing a scientific basis for achieving ideal load balancing and coordinated control. This method is not only applicable to the above-mentioned distributed power generation systems, but also provides valuable reference value for other types of multi-module parallel systems, promoting the development of intelligent power management and optimization control.

[0034] In a specific embodiment, the load balancing coordination analysis of the multi-module parallel network is performed based on the relative state feature set by the Lagrange multiplier method to obtain a load balancing coordination allocation scheme, including: Performing adaptive weight allocation on the relative state feature set to obtain a weighted feature matrix, and performing singular spectrum analysis on the weighted feature matrix to obtain a feature spectrum component set; By nonlinear programming, constraint conditions are constructed for the characteristic spectrum component set to obtain an optimization objective function, and Lagrangian multiplier expansion is performed on the optimization objective function to obtain a Lagrangian function set, which includes power balance constraints, voltage stability constraints and frequency synchronization constraints; The Lagrangian function set is iteratively solved based on the Lagrangian multiplier method to obtain an optimal solution sequence, and a load balancing coordination analysis is performed on the multi-module parallel network based on the optimal solution sequence to obtain a load balancing coordination allocation scheme; wherein the load balancing coordination allocation scheme includes an optimal power allocation scheme, an optimal voltage regulation scheme and an optimal frequency compensation scheme.

[0035] Specifically, in order to achieve load balancing coordination in a multi-module parallel network, it is crucial to conduct an in-depth analysis of the relative state feature set through the Lagrange multiplier method. This process not only requires comprehensive consideration of the dynamic characteristics of each module, but also requires ensuring that the system reaches the optimal state in terms of power balance, voltage stability, and frequency synchronization. Specifically, this step includes a series of complex technical processes from adaptive weight allocation to singular spectrum analysis, to nonlinear programming, Lagrange multiplier expansion, iterative solution, and finally obtaining a load balancing coordination allocation scheme. First, we adaptively weight the relative state feature set to obtain a weighted feature matrix, and perform singular spectrum analysis on the weighted feature matrix to obtain a feature spectrum component set. At this stage, considering that the relative state characteristics between different modules may differ significantly, we introduce an adaptive weight allocation mechanism. For example, in a distributed power generation system composed of multi-module parallel inverters, some inverters may exhibit different electrical characteristics and dynamic responses due to manufacturing tolerances or different working environments. By assigning corresponding weights to each feature, we can more accurately reflect these differences, thereby constructing a more reasonable "weighted feature matrix". Then, using singular spectrum analysis (SSA), we decompose this matrix into multiple characteristic spectrum components, each of which represents a specific pattern or trend in the original data. This analysis method can help us identify which features have the greatest impact on the overall performance of the system, and also provide us with a basis for further optimization. Next, through nonlinear programming, the constraint conditions of the characteristic spectrum component set are constructed to obtain the optimization objective function, and the Lagrange multiplier expansion of the optimization objective function is performed to obtain the Lagrangian function set. What is involved here is to convert the above characteristic spectrum components into a form that can be used for mathematical modeling. Nonlinear programming is a powerful tool that allows us to find the optimal solution under multiple constraints. In this case, we need to construct an optimization objective function that includes power balance constraints, voltage stability constraints, and frequency synchronization constraints. These constraints reflect the basic requirements that a multi-module parallel system must meet: the total output power remains constant, the voltage of each node is maintained within a safe range, and the operating frequency of all modules is consistent. By performing Lagrangian multiplier expansion on the optimization objective function, we convert it into a series of Lagrangian functions, which allows us to use the Lagrangian multiplier method to solve optimization problems with inequality constraints. For example, when we want a certain inverter to increase its output power, we must also ensure that it does not cause overload of other inverters or grid voltage fluctuations. Such multi-objective optimization problems can be effectively solved by the Lagrange multiplier method.Then, the Lagrangian function set is iteratively solved based on the Lagrangian multiplier method to obtain the optimal solution sequence, and the load balancing coordination analysis of the multi-module parallel network is performed based on the optimal solution sequence to obtain a load balancing coordination allocation scheme; wherein the load balancing coordination allocation scheme includes an optimal power allocation scheme, an optimal voltage regulation scheme, and an optimal frequency compensation scheme. This process is a process of approximating the optimal solution by repeatedly adjusting the variable values. Since power electronic systems are usually highly nonlinear, it is very necessary to use an iterative algorithm. Each iteration updates the parameters according to the gap between the current solution and the ideal solution until a solution that minimizes the objective function is found. In this process, we also need to continuously evaluate whether the obtained solution meets all constraints to verify the effectiveness of the algorithm. The final "optimal solution sequence" contains the optimal power allocation coefficient, voltage regulation coefficient, and frequency compensation coefficient, which together determine how each inverter should adjust its own working state to achieve the best performance of the entire system. For example, if an inverter is assigned a higher power allocation coefficient, it means that it should take on more output tasks; while a lower voltage regulation coefficient indicates that the inverter does not need to respond too much to the grid voltage. Based on these optimal solutions, we further conducted load balancing coordination analysis on the multi-module parallel network, and finally formed a complete set of "load balancing coordination allocation schemes". For example, in the distributed power generation system mentioned above, it is assumed that we have completed the adaptive weight allocation of the relative state feature set and obtained the feature spectrum component set through singular spectrum analysis. Then, we used nonlinear programming to construct the optimization objective function and performed Lagrange multiplier expansion to form a Lagrange function set. Through iterative solution, we found a set of optimal solution sequences, including the optimal power allocation scheme, the optimal voltage regulation scheme and the optimal frequency compensation scheme. Specifically, if an inverter has a strong power output capacity and stable operation, it can be assigned a higher power output task; for those inverters that are close to full load operation, their output needs to be appropriately reduced to avoid overload risks. At the same time, we will also fine-tune the voltage and frequency settings of each inverter according to the actual needs of the power grid to ensure the stability and efficiency of the entire system. In this way, we can achieve an ideal load balancing effect and ensure that the multi-module parallel network is always in an efficient and stable working state. In summary, the process of load balancing coordination analysis of the relative state feature set by Lagrange multiplier method is a highly complex optimization process that combines multiple advanced technologies such as adaptive weight allocation, singular spectrum analysis, nonlinear programming, Lagrange multiplier expansion and iterative solution. This process not only requires a deep mathematical foundation and technical support, but also requires us to flexibly apply these theoretical tools in practical applications to cope with various challenges in power electronic systems.Through this method, we can not only find the optimal solution that meets all constraints, but also ensure that these solutions are also optimal globally, providing a strong guarantee for load balancing and coordination of multi-module parallel networks. This method is not only applicable to the above-mentioned distributed power generation systems, but also provides valuable reference value for other types of power electronic equipment parallel systems, promoting the development of intelligent power management and optimization control.

[0036] In a specific embodiment, the optimization objective function is subjected to Lagrangian multiplier expansion to obtain a Lagrangian function set, including: Performing a dual transformation on the optimization objective function to obtain a dual constraint function group, and performing gradient projection on the dual constraint function group to obtain a constraint gradient field; Performing saddle point analysis on the constrained gradient field to obtain a saddle point solution sequence; wherein the saddle point solution sequence includes a power saddle point solution, a voltage saddle point solution and a frequency saddle point solution; Based on Wolfe duality theory, the saddle point solution sequence is Lagrangian expanded to obtain a set of Lagrangian functions.

[0037] Specifically, in order to deeply understand how to perform Lagrangian multiplier expansion on the optimization objective function and obtain the process of Lagrangian function set, we need to discuss in detail from duality transformation to gradient projection, and then to saddle point analysis, and finally complete each step of Lagrangian expansion based on Wolfe duality theory. This process not only involves complex mathematical operations and the application of optimization theory, but also provides a solid theoretical basis for load balancing coordination in multi-module parallel networks. First, we perform duality transformation on the optimization objective function to obtain a dual constraint function group, and perform gradient projection on the dual constraint function group to obtain a constraint gradient field. At this stage, our goal is to transform the original optimization problem into a form that is easier to solve. Through duality transformation, we can construct a new optimization problem, namely the "dual constraint function group", which is equivalent to the original problem in terms of optimal solution, but often has better mathematical properties. For example, in a distributed power generation system composed of multi-module parallel inverters, the original optimization objective function may contain multiple nonlinear constraints, such as power balance, voltage stability, and frequency synchronization. By introducing Lagrange multipliers, we integrate these constraints into the objective function to form an extended objective function. Then, we use the duality principle to reformulate this extended objective function, thereby obtaining a new set of constraints - the dual constraint function group. Next, in order to ensure that the solution obtained satisfies these constraints, we perform gradient projection on the dual constraint function group. Gradient projection is a method used to keep the solution within the feasible domain. It adjusts the variable values ​​to minimize the objective function value while ensuring that all constraints are satisfied. The result is the generation of a "constraint gradient field" that guides the direction of the subsequent optimization algorithm. Next, we perform saddle point analysis on the constraint gradient field to obtain a saddle point solution sequence; the saddle point solution sequence includes power saddle point solutions, voltage saddle point solutions, and frequency saddle point solutions. Saddle point analysis is an important means to identify local extreme points in optimization problems, especially in cases where there are multiple local optimal solutions. In this case, we focus on finding a solution that satisfies all constraints and minimizes the objective function. Due to the complexity and nonlinear characteristics of power electronic systems, it is usually difficult to directly find such a global optimal solution. Therefore, we use the method of saddle point analysis to explore possible local optimal solutions. Specifically, by analyzing the points in the constraint gradient field, we can determine which points are both local minima and local maxima, which are called "saddle points". For a multi-module parallel inverter system, this means that we have to find a set of parameter settings so that the output power, grid voltage and operating frequency of each inverter are within a reasonable range and the efficiency of the entire system is the highest. The resulting "saddle point solution sequence" corresponds to the power saddle point solution, voltage saddle point solution and frequency saddle point solution, which together constitute a potential set of optimal solutions.Then, based on the Wolfe duality theory, the saddle point solution sequence is Lagrangian expanded to obtain the Lagrangian function set. Wolfe duality theory provides an effective framework for evaluating and improving the performance of optimization algorithms, especially when dealing with constrained nonlinear programming problems. Based on this theory, we can further refine the saddle point solution sequence to ensure that the final solution is not only locally optimal, but also globally optimal. In specific operations, we use the Lagrangian multiplier method to expand the saddle point solution sequence. This step involves treating each saddle point solution as an optimal solution under a specific condition and considering the influence of other constraints by introducing additional Lagrangian multipliers. For example, if we have found a saddle point solution that satisfies power balance and voltage stability, then we need to check whether it also meets the requirements of frequency synchronization. In this way, we can construct a complete set of Lagrangian functions that comprehensively reflect the optimal solutions under all constraints. The final "Lagrangian function set" not only provides a clear goal for subsequent iterative solutions, but also lays a solid theoretical foundation for realizing load balancing and coordination of multi-module parallel networks. For example, in the distributed generation system mentioned above, assume that we have completed the dual transformation of the optimization objective function and obtained a set of dual constraint functions. By gradient projection of these constraint functions, we obtain the constraint gradient field, which points out the optimization direction for us. Next, through saddle point analysis, we find a series of potential optimal solutions, including power saddle point solutions, voltage saddle point solutions, and frequency saddle point solutions. Finally, based on the Wolfe duality theory, we perform Lagrangian expansion on these saddle point solutions and obtain the final set of Lagrangian functions. In this process, we not only ensure that the working state of each inverter is optimal, but also verify the global validity of these states. For example, if an inverter performs best in terms of power output, but we find that its voltage regulation or frequency compensation capabilities are insufficient, then through Lagrangian expansion, we can adjust the relevant parameters to achieve the best overall performance in the entire system. In this way, we can achieve an ideal load balancing effect and ensure that the entire multi-module parallel network operates efficiently and stably. In summary, the process of Lagrange multiplier expansion of the optimization objective function is a complex optimization process that combines duality transformation, gradient projection, saddle point analysis, and the application of Wolfe duality theory. This process requires not only a deep mathematical foundation and technical support, but also requires us to flexibly apply these theoretical tools in practical applications to meet the various challenges in power electronic systems. In this way, we can not only find the optimal solution that meets all constraints, but also ensure that these solutions are also optimal globally, providing a strong guarantee for achieving load balancing coordination of multi-module parallel networks.This method is not only applicable to the distributed power generation systems mentioned above, but also provides valuable reference value for other types of power electronic equipment parallel systems, promoting the development of intelligent power management and optimization control.

[0038] In a specific embodiment, the load balancing coordination allocation scheme is converted into control instructions to obtain a control instruction sequence of each module, including: Performing pulse width modulation conversion on the load balancing coordination allocation scheme by space vector decomposition to obtain a switching timing sequence; Dynamically compensating the switch timing sequence based on a state feedback matrix to obtain a compensation control amount, and performing discrete quantization processing on the compensation control amount to obtain a digital control word; Performing feedforward prediction on the digital control word based on a preset prediction controller to obtain a prediction control sequence; Decoupling the prediction control sequence in parallel through a module decoupler to obtain an independent control quantity, and coordinating the independent control quantity in time sequence to obtain a synchronous control sequence; The synchronous control sequence is hardware mapped based on the instruction generator to obtain the control instruction sequence of each module.

[0039] Specifically, in order to convert the load balancing coordination allocation scheme into specific control instructions so that it can be understood and executed by each module, the whole process needs to go through a series of delicate conversion and optimization steps. These steps include pulse width modulation conversion through space vector decomposition, dynamic compensation based on state feedback matrix, discrete quantization processing, feedforward prediction, parallel decoupling and final hardware mapping. Each link is closely connected to ensure that the optimized load distribution strategy can be implemented in the actual system. First, we convert the load balancing coordination allocation scheme into pulse width modulation (PWM) through space vector decomposition to obtain the switching timing sequence. At this stage, we need to convert the abstract load distribution scheme into specific operation instructions, that is, how to adjust the output power of each inverter. Space vector decomposition is a technology widely used in the field of power electronics. It can represent three-phase AC signals as rotating vectors on a two-dimensional plane, thereby simplifying the design of PWM control. For example, in a distributed power generation system consisting of multiple parallel inverters, suppose we have determined that a certain inverter needs to increase its output power. Then, through space vector decomposition, we can calculate the corresponding PWM duty cycle and generate a set of "switching timing sequences" that accurately specify the on-time and off-time of the inverter in each cycle. This control method can not only efficiently regulate the output power, but also ensure the quality of the current waveform and reduce harmonic distortion. Next, the switching timing sequence is dynamically compensated based on the state feedback matrix to obtain the compensation control quantity, and the compensation control quantity is discretely quantized to obtain a digital control word. Due to the dynamic characteristics of the power electronic system, simple PWM control may not be able to fully adapt to rapidly changing working conditions. Therefore, we introduce a state feedback mechanism to enhance control accuracy. The state feedback matrix can adjust the control parameters in real time according to the current system operating state (such as voltage, current, etc.) to offset the impact of external disturbances or internal changes. For example, if the grid voltage fluctuates suddenly, the state feedback matrix will respond quickly and stabilize the output by modifying the PWM duty cycle. In addition, in order to make the control system compatible with digital hardware, we also need to discretely quantize the compensation control quantity, that is, convert the continuous analog signal into a digital signal with a finite number of bits to form a "digital control word". This process ensures that all control instructions can be accurately executed by digital devices, while also improving the system's anti-interference ability and stability. Then, the digital control word is feedforward predicted based on the predictive controller to obtain a predictive control sequence. Predictive control is an advanced control strategy that uses mathematical models to predict system behavior over a period of time in the future and adjust control inputs in advance accordingly. For a multi-module parallel inverter system, this means that we can pre-calculate the best actions that each inverter should take in the next few cycles based on the current load demand and grid conditions.For example, if we anticipate an upcoming peak load period, the predictive controller can increase the output power of some inverters in advance to avoid overload. In this way, we can not only improve the response speed of the system, but also better cope with complex and changing working environments to ensure the smooth operation of the entire system. Next, the predictive control sequence is parallel decoupled by the module decoupler to obtain independent control quantities, and the independent control quantities are time-coordinated to obtain a synchronous control sequence. Since there may be mutual influence between modules in a multi-module parallel system, the direct application of the predictive control sequence may lead to unexpected behavior. Therefore, we need to use a module decoupler to separate the originally associated control instructions into their own independent parts, namely "independent control quantities". Doing so can not only eliminate the coupling effect between modules, but also enable each inverter to work according to its own optimal solution. However, in order to ensure the synergy between all inverters, we also need to time-coordinate these independent control quantities to generate a set of "synchronous control sequences". For example, in a distributed power generation system consisting of multiple inverters, even if each inverter has its own control logic, we must ensure that their actions are synchronized to maintain the stability and efficiency of the entire system. Finally, the synchronous control sequence is hardware mapped based on the instruction generator to obtain the control instruction sequence of each module. The function of the instruction generator is to convert all the above optimization results into specific hardware commands, which can be directly sent to the control unit of each inverter. For example, it may map each element in the synchronous control sequence to the value of a specific register, or convert it into a data frame format under the communication protocol. In this way, after each inverter receives its own control instruction, it can immediately start to adjust its own operating parameters, such as PWM duty cycle, reference voltage setting value, etc., to adapt to the new load requirements. Through this hardware mapping, we not only achieve an effective transition from theory to practice, but also ensure that all control instructions can be accurately transmitted and executed. In summary, the process of converting the load balancing coordination allocation scheme into control instructions is a multi-level and multi-step technical implementation process. It combines multiple advanced technologies such as space vector decomposition, state feedback, discrete quantization, predictive control, module decoupling and timing coordination, aiming to ensure that the optimized load distribution strategy can be accurately implemented in actual power electronic systems. This method is not only applicable to the above-mentioned distributed generation system, but also provides valuable reference value for other types of multi-module parallel systems, promoting the development of intelligent power management and optimization control. In this way, we can ensure that the multi-module parallel network is always in an efficient and stable working state to meet the growing needs of modern power systems.

[0040] In a specific embodiment, the control instruction sequence is parsed and implemented in each module to achieve load balancing of the multi-module parallel network, including: Performing real-time slicing processing on the control instruction sequence to obtain a time-slicing instruction set, and performing parallel scheduling analysis on the time-slicing instruction set to obtain a module execution queue; Prioritizing the module execution queue to obtain a priority execution sequence, and performing deadlock detection and elimination on the priority execution sequence to obtain a deadlock-free execution sequence; Distributing instructions for the deadlock-free execution sequence based on a preset distributed message bus to obtain a module control message, and generating a CRC check code for the module control message to obtain a reliable transmission frame; Performing state conversion on the reliable transmission frame to obtain an execution state sequence, The execution state sequence is implemented in each module to achieve load balancing of the multi-module parallel network.

[0041] Specifically, in order to ensure that the control instruction sequence can be accurately parsed and effectively implemented in each module to achieve load balancing of a multi-module parallel network, the entire process needs to go through a series of closely connected steps such as real-time slicing processing, parallel scheduling analysis, priority sorting, deadlock detection and elimination, distributed message bus instruction distribution, CRC check code generation, and state transition. These steps together constitute an efficient and reliable control instruction execution framework, ensuring that the optimized load distribution strategy can be accurately applied in the actual system. First, we perform real-time slicing processing on the control instruction sequence to obtain a time-slicing instruction set, and perform parallel scheduling analysis on the time-slicing instruction set to obtain a module execution queue. At this stage, we need to divide the original continuous control instruction sequence into multiple smaller time segments, each of which contains a set of operation instructions that need to be executed within a specific time period. For example, in a distributed power generation system composed of multi-module parallel inverters, assume that we have generated a string of control instructions based on the load balancing coordination allocation scheme, and these instructions specify the specific operations of each inverter in the future. Through real-time slicing processing, we can divide this string of instructions into several "time-slicing instruction sets", each of which corresponds to a shorter time window. Next, in order to make full use of the parallel processing capabilities of the system, we perform parallel scheduling analysis on these time-sliced ​​instruction sets, evaluate the dependencies and execution order between each instruction, and finally build a set of "module execution queues". This not only improves the efficiency of instruction execution, but also enables the system to better adapt to rapidly changing working conditions. Then, the module execution queues are prioritized to obtain a priority execution sequence, and the priority execution sequence is deadlock detected and eliminated to obtain a deadlock-free execution sequence. Since there may be resource competition or task priority differences between different modules, simple sequential execution may not achieve the optimal effect. Therefore, we introduced a priority sorting mechanism to dynamically adjust the task priority of each module according to the current system operation status and task urgency. For example, if an inverter is processing a critical task, such as maintaining grid voltage stability, its priority may be higher than that of other inverters responsible for conventional power output. In addition, in order to avoid deadlock caused by improper task scheduling, we also need to detect and eliminate deadlock on the priority execution sequence. Deadlock refers to a state where two or more tasks wait for each other to release resources and fall into an infinite waiting state. By adopting preventive algorithms (such as the banker's algorithm) or detection and recovery mechanisms, we can identify potential deadlock situations and take measures to resolve them, thereby obtaining a set of "deadlock-free execution sequences". Then, based on the distributed message bus, the deadlock-free execution sequence is distributed with instructions to obtain module control messages, and CRC checksums are generated for the module control messages to obtain reliable transmission frames.Distributed message bus is a technology used to transmit information in distributed systems. It can provide efficient and reliable message communication services between different hardware components. In this example, we encapsulate each control instruction in the deadlock-free execution sequence into a "module control message" and send it to the corresponding inverter through the distributed message bus. In order to ensure that these instructions will not be erroneous or lost during transmission, we also add a cyclic redundancy check (CRC) check code to each control message. CRC check code is an error detection method widely used in the field of data transmission. It can effectively detect bit errors that occur during data transmission. Once the receiving end receives the control message with CRC check code, it will recalculate the check value and compare it with the received check code. If the two are consistent, it means that the data transmission is correct. Otherwise, it indicates that there is an error and needs to be resent. In this way, we generate a series of "reliable transmission frames" to provide a solid guarantee for subsequent instruction execution. Subsequently, the reliable transmission frame is state-transitioned to obtain an execution state sequence. State transition refers to adjusting the current working state of the system according to the received control instruction to meet the expected goal. At this stage, after each inverter receives its own reliable transmission frame, it will update its own operating parameters, such as PWM duty cycle, reference voltage setting value, etc. according to the control instructions therein. At the same time, it will also record the changes in these parameters to form a set of "execution state sequences". For example, when an inverter receives an instruction to increase the output power, it will adjust its PWM duty cycle accordingly and record this change for subsequent monitoring and feedback. The execution state sequence not only reflects the actual response of each inverter to the control instruction, but also provides an important basis for the overall performance evaluation of the system. Finally, the execution state sequence is implemented in each module to achieve load balancing of the multi-module parallel network. Once all inverters have completed the state transition and adjusted their own working state according to the predetermined plan, the entire multi-module parallel network achieves an ideal load balancing effect. For example, in the above-mentioned distributed power generation system, through this series of carefully designed steps, we ensure that each inverter can work efficiently according to the optimized load distribution plan, which not only avoids the risk of overloading of individual modules, but also improves the operating efficiency and stability of the entire system. In this way, we have successfully transformed the abstract load balancing coordination allocation scheme into a specific control behavior, achieving an effective transition from theory to practice. In summary, parsing the control instruction sequence and implementing it into each module is a complex process involving multiple technical links. It combines multiple advanced technologies such as real-time sharding processing, parallel scheduling analysis, priority sorting, deadlock detection and elimination, distributed message bus instruction distribution, CRC checksum generation, and state transition to ensure that the optimized load distribution strategy can be accurately applied in actual power electronic systems.This method is not only applicable to the above-mentioned distributed generation system, but also provides valuable reference value for other types of multi-module parallel systems, promoting the development of intelligent power management and optimization control. In this way, we can ensure that the multi-module parallel network is always in an efficient and stable working state to meet the growing needs of modern power systems.

[0042] The above describes the multi-module parallel balanced coordinated control method in the embodiment of the present invention. The following describes the multi-module parallel balanced coordinated control system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a multi-module parallel balanced coordinated control system includes: The acquisition module 21 is used to synchronously acquire the operating parameters of the multi-module parallel network through a preset high-speed synchronous sampler to obtain original state data; A first analysis module 22, configured to perform a timing decoupling analysis on the multi-module parallel network based on the original state data to obtain a relative state feature set between modules; The second analysis module 23 is used to perform load balancing coordination analysis on the multi-module parallel network based on the relative state feature set by Lagrange multiplier method to obtain a load balancing coordination allocation scheme if the relative state feature set is not within the respective preset state range; The conversion module 24 is used to convert the load balancing coordination allocation scheme into control instructions to obtain a control instruction sequence of each module; The implementation module 25 is used to parse the control instruction sequence and implement it to each module to achieve load balancing of the multi-module parallel network.

[0043] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0044] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0046] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0048] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-module parallel balanced coordinated control method, characterized in that: Applied to a multi-module parallel network, the multi-module parallel network includes a plurality of modules connected to each other, including the following steps: The operating parameters of the multi-module parallel network are synchronously collected by a preset high-speed synchronous sampler to obtain original state data; Based on the original state data, a timing decoupling analysis is performed on the multi-module parallel network to obtain a relative state feature set between modules; If the relative state feature set is not within the respective preset state range, a load balancing coordination analysis is performed on the multi-module parallel network based on the relative state feature set by Lagrange multiplier method to obtain a load balancing coordination allocation scheme; The load balancing coordination and allocation scheme is converted into control instructions to obtain a control instruction sequence for each module; The control instruction sequence is parsed and implemented in each module to achieve load balancing of the multi-module parallel network.

2. The multi-module parallel balanced coordinated control method according to claim 1, characterized in that: The operation parameters of the multi-module parallel network are synchronously collected by a preset high-speed synchronous sampler to obtain original state data, including: The voltage and current signals of each module are sampled by a high-speed synchronous sampler to obtain an original sampling sequence; Performing phase compensation processing on the original sampling sequence based on a time synchronization trigger to obtain a synchronous sampling data stream; Performing multi-scale decomposition on the synchronous sampling data stream by a wavelet packet decomposer to obtain multi-scale feature data; The multi-scale feature data is subjected to spectrum analysis based on discrete Fourier transform to obtain original state data; wherein the original state data includes fundamental wave parameters, harmonic content and phase sequence relationship of each module, and the phase sequence relationship is the phase sequence relationship between the three-phase voltage and the three-phase current inside each module.

3. The multi-module parallel balanced coordinated control method according to claim 1, characterized in that: The performing of timing decoupling analysis on the multi-module parallel network based on the original state data to obtain a relative state feature set between modules includes: Performing frequency domain transformation on the original state data through a preset frequency domain decomposer to obtain a frequency domain feature vector, and performing principal component analysis on the frequency domain feature vector to obtain a frequency domain principal component set; Performing Hilbert transform on the frequency domain principal component set to obtain an instantaneous frequency sequence; Performing state estimation on the instantaneous frequency sequence by using Kalman filtering to obtain an estimated state quantity, and performing state space model analysis on the estimated state quantity to obtain a state transfer matrix; Based on the state transfer matrix, the multi-module parallel network is time-sequentially decoupled to obtain a decoupled state quantity, and a covariance analysis is performed on the decoupled state quantity to obtain a dynamic coupling relationship between modules; The dynamic coupling relationship between the modules is classified by fuzzy clustering analysis to obtain clustering results, and the clustering results are subjected to inter-class similarity analysis to obtain a relative state feature set between the modules; wherein the relative state feature set includes power state characteristics, electrical state characteristics and dynamic state response.

4. The multi-module parallel balanced coordinated control method according to claim 1, characterized in that: The method of performing load balancing coordination analysis on the multi-module parallel network based on the relative state feature set by using the Lagrange multiplier method to obtain a load balancing coordination allocation scheme includes: Performing adaptive weight allocation on the relative state feature set to obtain a weighted feature matrix, and performing singular spectrum analysis on the weighted feature matrix to obtain a feature spectrum component set; Constructing constraint conditions for the characteristic spectrum component set through nonlinear programming to obtain an optimization objective function, and performing Lagrangian multiplier expansion on the optimization objective function to obtain a Lagrangian function set, wherein the Lagrangian function set includes a power balance constraint, a voltage stability constraint, and a frequency synchronization constraint; Iteratively solving the Lagrangian function set based on the Lagrangian multiplier method to obtain an optimal solution sequence; Based on the optimal solution sequence, a load balancing coordination analysis is performed on the multi-module parallel network to obtain a load balancing coordination allocation scheme; wherein the load balancing coordination allocation scheme includes an optimal power allocation scheme, an optimal voltage regulation scheme and an optimal frequency compensation scheme.

5. The multi-module parallel balanced coordinated control method according to claim 4 is characterized in that: The Lagrangian multiplier expansion is performed on the optimization objective function to obtain a Lagrangian function set, including: Performing a dual transformation on the optimization objective function to obtain a dual constraint function group, and performing gradient projection on the dual constraint function group to obtain a constraint gradient field; Performing saddle point analysis on the constrained gradient field to obtain a saddle point solution sequence; wherein the saddle point solution sequence includes a power saddle point solution, a voltage saddle point solution and a frequency saddle point solution; Based on Wolfe duality theory, the saddle point solution sequence is Lagrangian expanded to obtain a set of Lagrangian functions.

6. The multi-module parallel balanced coordinated control method according to claim 1, characterized in that: The load balancing coordination and allocation scheme is converted into control instructions to obtain a control instruction sequence of each module, including: Performing pulse width modulation conversion on the load balancing coordination allocation scheme by space vector decomposition to obtain a switching timing sequence; Dynamically compensating the switch timing sequence based on a state feedback matrix to obtain a compensation control amount, and performing discrete quantization processing on the compensation control amount to obtain a digital control word; Performing feedforward prediction on the digital control word based on a preset prediction controller to obtain a prediction control sequence; Decoupling the prediction control sequence in parallel through a module decoupler to obtain an independent control quantity, and coordinating the independent control quantity in time sequence to obtain a synchronous control sequence; The synchronous control sequence is hardware mapped based on the instruction generator to obtain the control instruction sequence of each module.

7. The multi-module parallel balanced coordinated control method according to claim 1, characterized in that: The process of parsing the control instruction sequence and implementing it in each module to achieve load balancing of the multi-module parallel network includes: Performing real-time slicing processing on the control instruction sequence to obtain a time-slicing instruction set, and performing parallel scheduling analysis on the time-slicing instruction set to obtain a module execution queue; Prioritizing the module execution queue to obtain a priority execution sequence, and performing deadlock detection and elimination on the priority execution sequence to obtain a deadlock-free execution sequence; Distributing instructions for the deadlock-free execution sequence based on a preset distributed message bus to obtain a module control message, and generating a CRC check code for the module control message to obtain a reliable transmission frame; And the reliable transmission frame is converted into a state to obtain an execution state sequence, The execution state sequence is implemented in each module to achieve load balancing of the multi-module parallel network.

8. A multi-module parallel balanced coordination control device, characterized in that: Applicable to a multi-module parallel network, the multi-module parallel network includes a plurality of modules connected to each other, including: An acquisition module, used for synchronously acquiring the operating parameters of the multi-module parallel network through a preset high-speed synchronous sampler to obtain original state data; A first analysis module, configured to perform a timing decoupling analysis on the multi-module parallel network based on the original state data to obtain a relative state feature set between modules; A second analysis module is used to perform load balancing coordination analysis on the multi-module parallel network based on the relative state feature set by Lagrange multiplier method to obtain a load balancing coordination allocation scheme if the relative state feature set is not within the respective preset state ranges; A conversion module, used to convert the load balancing coordination allocation scheme into control instructions to obtain a control instruction sequence for each module; The implementation module is used to parse the control instruction sequence and implement it to each module to achieve load balancing of the multi-module parallel network.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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