Intelligent low-voltage compensation cabinet

Abstract

The invention relates to the technical field of power management, in particular to an intelligent low-voltage compensation cabinet, which is characterized in that an electrical connection diagram of the intelligent low-voltage compensation cabinet and basic setting parameters of compensation modules are acquired, electrical characteristic difference analysis among the compensation modules is carried out based on the electrical connection diagram, and electrical characteristic difference data of the modules are acquired; performing compensation current distribution deviation simulation analysis on the module electrical characteristic difference data according to the basic setting parameters of the compensation module to obtain compensation current distribution deviation data; predicting life attenuation by combining a heat accumulation model to obtain module life influence associated data; and then control parameter space limiting matching is carried out, compensation module output control planning design is carried out through control parameter space limiting matching data, control planning data is output, the module overload risk caused by uneven current distribution is remarkably reduced, the operation reliability of the compensation system is improved, and the system reliability is improved. Equipment damage and fire hazards caused by local overheating are avoided, and energy loss caused by circulation is effectively restrained.

Description

Technical Field

[0001] This invention belongs to the field of power management technology and relates to an intelligent low-voltage compensation cabinet. Background Technology

[0002] To achieve high-capacity compensation and higher reliability, intelligent low-voltage compensation cabinets often employ multiple compensation modules operating in parallel. Ideally, these modules should distribute the compensation tasks evenly. However, the parallel operation control of current intelligent low-voltage compensation cabinets still faces several technical bottlenecks that urgently need improvement, mainly in the following two aspects: 1. Existing current sharing control strategies for compensation modules fail to establish a multi-dimensional collaborative control mechanism. Relying solely on simple current distribution algorithms is insufficient to address the dynamic compensation needs under complex operating conditions. This simplistic control approach leads to mismatch in the output characteristics of each module, resulting in increased system circulating current and decreased compensation accuracy. This not only affects the effectiveness of power quality management but also triggers a vicious cycle between modules, accelerating equipment aging and significantly increasing system operation and maintenance costs and safety risks.

[0003] 2. The current system lacks the ability to accurately perceive the operating status of modules and predict their lifespan, making it impossible to achieve real-time assessment and early warning of module health. This lack of status monitoring makes it difficult for the system to detect potential faults in a timely manner. It cannot effectively prevent system-level accidents caused by single module failures, nor can it provide data support for preventive maintenance. Ultimately, this affects the continuity and reliability of the power supply system, causing unnecessary power outage losses and economic risks to users. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention provides an intelligent low-voltage compensation cabinet to solve the above-mentioned technical problems.

[0005] To achieve the above and other objectives, the technical solution adopted by the present invention is as follows: This invention provides an intelligent low-voltage compensation cabinet, comprising: Electrical data acquisition module: acquires the electrical connection diagram of the intelligent low-voltage compensation cabinet and the basic setting parameters of the compensation modules, including rated current, rated voltage, impedance characteristics, heat dissipation coefficient and response time; based on the electrical connection diagram, it performs electrical characteristic difference analysis between compensation modules, and obtains module electrical characteristic difference data by calculating the equivalent impedance deviation, phase angle difference and response time delay of each compensation module; Compensation Module Analysis Module: Based on the basic setting parameters of the compensation module, the module performs a simulation analysis of the difference in electrical characteristics of the module to obtain the compensation current distribution deviation data by simulating the current circulation, thermal parameter drift and transient response misalignment when multiple modules are running in parallel. Based on the compensation current distribution deviation data, the module lifespan impact correlation is mined. By correlating the current unevenness with the module aging rate and combining the thermal accumulation model to predict lifespan decay, the module lifespan impact correlation data is obtained. Compensation data output module: Based on the correlation data of module lifespan impact, control parameter space limit matching is performed. By matching the gain, limiting value and phase compensation parameters of the current sharing control loop, control parameter space limit matching data is generated. The output control planning design of the compensation module is performed based on the control parameter space limit matching data. By adjusting the PWM duty cycle, switching frequency and fan control signal of each compensation module, the output control planning data of the compensation module is obtained.

[0006] As described above, the intelligent low-voltage compensation cabinet provided by the present invention has at least the following beneficial effects: This invention provides an intelligent low-voltage compensation cabinet. By acquiring the electrical connection diagram and basic setting parameters of the compensation modules, it analyzes the differences in electrical characteristics among the compensation modules and calculates the corresponding compensation current distribution deviation data. This determines the correlation between module lifespan and the output control planning scheme for each compensation module, effectively solving the problem of insufficient current sharing control accuracy in current intelligent low-voltage compensation cabinets operating with multiple modules in parallel. On one hand, by analyzing electrical characteristics from a multi-parameter fusion perspective, it significantly reduces the risk of module overload caused by uneven current distribution, improves the reliability of the compensation system, avoids equipment damage and fire hazards caused by localized overheating, and effectively suppresses energy loss caused by circulating current, improving the overall energy efficiency of the system. On the other hand, through accurate lifespan prediction and dynamic control strategies, it significantly extends the service life of the compensation modules, reduces equipment maintenance costs and replacement frequency, and ensures stable operation of the compensation system under various operating conditions, guaranteeing power quality while reducing damage to electrical equipment caused by voltage fluctuations. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a schematic diagram showing the connection of each module of the low-voltage compensation cabinet of the present invention. Detailed Implementation

[0009] The following description, in conjunction with the implementation of this invention, is merely an example and illustration of the concept of this invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in these claims, all of which should fall within the protection scope of this invention.

[0010] Example 1 Please see Figure 1 As shown, an intelligent low-voltage compensation cabinet includes an electrical data acquisition module, a compensation module analysis module, and a compensation data output module. The modules described above are connected via wired and / or wireless means to enable data transmission between them. Electrical data acquisition module: Acquires the electrical connection diagram of the intelligent low-voltage compensation cabinet and the basic setting parameters of the compensation modules, including rated current, rated voltage, impedance characteristics, heat dissipation coefficient, and response time. Based on the electrical connection diagram, it analyzes the differences in electrical characteristics between compensation modules, and obtains the module electrical characteristic difference data by calculating the equivalent impedance deviation, phase angle difference, and response time delay of each compensation module.

[0011] For example, the working logic of the electrical data acquisition module is as follows: Obtain the electrical connection diagram and basic setting parameters of the intelligent low-voltage compensation cabinet, including rated current, rated voltage, impedance characteristics, heat dissipation coefficient and response time. The electrical connection diagram is vectorized to generate an electrical connection vector diagram. The electrical connection vector diagram is analyzed to obtain the connection relationship data of the compensation modules. The connection relationship data of the compensation modules includes the installation position of each compensation module in the cabinet, the electrical connection topology and parallel node information. Based on the connection relationship data and basic setting parameters of the compensation modules, the electrical characteristic differences between the compensation modules are analyzed. The equivalent impedance deviation, phase angle difference, and response time delay of each compensation module are calculated to obtain the electrical characteristic difference data of the modules. Among them, the equivalent impedance deviation is calculated by comparing the absolute difference between the equivalent impedance value of each compensation module and the average equivalent impedance value; the phase angle difference is obtained by calculating the difference between the phase angle between the output current and voltage of each compensation module and the reference phase angle; and the response time delay is obtained by calculating the difference between the response time of each compensation module and the minimum response time.

[0012] In this embodiment of the invention, the electrical connection diagram and basic setting parameters of the compensation module of the intelligent low-voltage compensation cabinet are obtained. The basic setting parameters of the compensation module include rated current, rated voltage, impedance characteristics, heat dissipation coefficient, and response time. When vectorizing the electrical connection diagram, a graph-based vectorization algorithm is used to convert the nodes and lines in the original electrical connection diagram into vector data format, generating an electrical connection vector diagram. This process ensures the accuracy and parsability of the vector diagram by identifying electrical component symbols and connection paths in the diagram and utilizing boundary tracking and coordinate transformation methods. Subsequently, the connection relationship of the compensation module is parsed on the electrical connection vector diagram. Through topology analysis and adjacency matrix calculation, the installation position of each compensation module within the cabinet, the electrical connection topology, and parallel node information are extracted to generate compensation module connection relationship data. The installation position is determined by parsing the spatial coordinates and relative distances in the vector diagram; the electrical connection topology is constructed by analyzing the connection types and path directions between nodes; and the parallel node information is obtained by identifying shared electrical connection points and their branch relationships. Based on the connection relationship data and basic setting parameters of the compensation modules, an analysis of the differences in electrical characteristics among the compensation modules is performed: First, the equivalent impedance deviation is calculated by comparing the equivalent impedance value of each compensation module with the average equivalent impedance value of all modules, and taking the absolute difference as the deviation result; Second, the phase angle difference is calculated by measuring the phase angle between the output current and voltage of each compensation module and obtaining the difference with the preset reference phase angle; Finally, the response time delay is calculated by calculating the difference between the response time of each compensation module and the minimum response time among all modules.

[0013] Compensation Module Analysis Module: Based on the basic setting parameters of the compensation module, this module performs a simulation analysis of the difference in electrical characteristics of the modules to obtain compensation current distribution deviation data. By simulating the current circulation, thermal parameter drift, and transient response misalignment during the parallel operation of multiple modules, compensation current distribution deviation data is obtained. Based on the compensation current distribution deviation data, the module lifespan impact correlation is mined. By correlating current unevenness with the module aging rate and combining it with a thermal accumulation model to predict lifespan decay, the module lifespan impact correlation data is obtained.

[0014] For example, the implementation logic of the compensation module analysis module includes: Step S21: Perform a multi-module parallel current circulation simulation based on the basic setting parameters of the compensation module and the difference data of the module electrical characteristics. By calculating the deviation between the current distribution ratio of each compensation module and the ideal ratio, the current circulation distribution data is obtained. Step S22: Based on the current circulation distribution data and the heat dissipation coefficient in the basic setting parameters of the compensation module, perform thermal parameter drift simulation. By simulating the temperature rise and parameter changes caused by uneven current, obtain thermal parameter drift data. Step S23: Perform transient response misalignment simulation based on thermally induced parameter drift data, module electrical characteristic difference data, and current circulation distribution data. By analyzing the impact of parameter drift and response time delay on dynamic compensation, transient response misalignment data is obtained. Step S24: Based on the current circulation distribution data, thermally induced parameter drift data and transient response offset data, perform a comprehensive calculation of the compensation current distribution deviation. By weighted fusion of circulation, drift and offset factors, the compensation current distribution deviation data is obtained. Step S25: Perform module aging rate correlation analysis based on compensation current distribution deviation data. By correlating current deviation with aging acceleration factor, obtain module aging rate correlation data. Step S26: Based on the module aging rate correlation data and the heat dissipation coefficient in the basic setting parameters of the compensation module, perform thermal accumulation model lifetime prediction. By simulating the decay effect of long-term thermal accumulation on lifetime, obtain the correlation data of module lifetime impact.

[0015] It should be added that step S21 includes the following steps: Step S211: Calculate the equivalent impedance deviation of each compensation module based on the module electrical characteristic difference data. By comparing the absolute difference between the equivalent impedance value of each module and the average equivalent impedance value, obtain the equivalent impedance deviation data of each module. Step S212: Simulate the current distribution of the parallel circuit based on the equivalent impedance deviation data of each module and the rated current in the basic setting parameters of the compensation module. By simulating the current output ratio of each module in the parallel circuit, obtain the actual current distribution ratio data. Step S213: Calculate the ideal current distribution ratio based on the rated current and rated voltage in the basic setting parameters of the compensation module. Obtain the ideal current distribution ratio data by distributing the total compensation current proportionally according to the rated capacity of the module. Step S214: Perform current distribution deviation analysis based on actual current distribution ratio data and ideal current distribution ratio data. By calculating the difference between the actual ratio and the ideal ratio of each module, obtain the current circulation distribution data.

[0016] In this embodiment of the invention, the equivalent impedance deviation of each compensation module is calculated based on the differences in the electrical characteristics of the modules. Specifically, the arithmetic mean of the equivalent impedance values ​​of all compensation modules is calculated as a benchmark. Then, for each module, the absolute difference between its equivalent impedance value and this average value is calculated to obtain the equivalent impedance deviation data of each module. Subsequently, based on the equivalent impedance deviation data of each module and the rated current in the basic setting parameters of the compensation modules, a parallel circuit current distribution simulation is performed. The operating principle is that the current distribution in a parallel circuit is inversely proportional to the module impedance. By establishing a circuit model based on Ohm's law and Kirchhoff's current law, and correcting the ideal impedance by combining the equivalent impedance deviation values ​​of each module, the actual output current value of each module under a given total compensation current condition is simulated, and the ratio of the output current of each module to the total current is calculated to obtain the actual current distribution ratio. Next, based on the rated current and rated voltage in the basic setting parameters of the compensation module, the ideal current distribution ratio is calculated. The operating principle is based on the principle of proportional distribution of the module's rated capacity. First, the rated capacity value of the module is obtained by multiplying the rated current and rated voltage of each module. Then, the sum of the rated capacities of all modules is calculated as the total rated capacity. Finally, the ratio of the rated capacity value of each module to the total rated capacity value is used as the ideal current distribution ratio of the module, thus obtaining the ideal current distribution ratio data. Finally, based on the actual current distribution ratio data and the ideal current distribution ratio data, current distribution deviation analysis is performed. By calculating the algebraic difference between the actual current distribution ratio value and the ideal current distribution ratio value for each compensation module, the current distribution deviation value of each module is obtained. The collection of all these deviation values ​​constitutes the current circulation distribution data.

[0017] It should be added that step S22 includes the following steps: Step S221: Calculate the actual current value of each module based on the current circulation distribution data and the rated current in the basic setting parameters of the compensation module. The actual current data of each module is obtained by adding the current distribution ratio deviation to the ideal current ratio and multiplying it by the total rated current. Step S222: Calculate heat generation based on the equivalent impedance in the actual current data and electrical characteristic difference data of each module. Obtain the heat generation data of each module by calculating the product relationship between the square of the current and the equivalent impedance value of each module. Step S223: Based on the heat generation data of each module and the heat dissipation coefficient in the basic setting parameters of the compensation module, perform temperature rise simulation, calculate steady-state temperature value by simulating the balance relationship between heat generation and heat dissipation coefficient, and obtain temperature rise data of each module; Step S224: Perform parameter drift simulation based on the temperature rise data of each module. By simulating the linear influence of temperature rise on equivalent impedance and response time, obtain thermally induced parameter drift data.

[0018] In this embodiment of the invention, the actual current value of each module is calculated based on the current circulation distribution data and the rated current in the basic setting parameters of the compensation module. The operating principle is to correct the ideal current distribution by the deviation of the current distribution ratio. Specifically, the deviation value of the current distribution ratio of each module (i.e., the algebraic difference between the actual current distribution ratio and the ideal current distribution ratio) is added to the ideal current distribution ratio of the module to obtain the corrected current distribution ratio. Then, this ratio is multiplied by the total rated current of the system (i.e., the sum of the rated currents of all compensation modules) to obtain the actual current data of each module. Subsequently, heat generation is calculated based on the equivalent impedance in the actual current data of each module and the difference data of module electrical characteristics. The operating principle is based on Joule's law, that is, the heat generated in a resistive element is proportional to the product of the square of the current and the resistance. By calculating the product of the square of the actual current value of each module and the equivalent impedance value of the module, the heat generation data generated by each module per unit time is obtained. Next, temperature rise simulation is performed based on the heat generation data of each module and the heat dissipation coefficient in the basic setting parameters of the compensation module. The operating principle is to establish a thermal balance model, in which the temperature rise of the module depends on the dynamic balance between the heat generation rate and the heat dissipation capacity. By dividing the heat generation value of each module by its corresponding heat dissipation coefficient (the heat dissipation coefficient represents the heat dissipation power of the module under unit temperature difference), and considering the ambient temperature reference, the temperature rise data of each module under steady-state conditions is calculated. Finally, parameter drift simulation is performed based on the temperature rise data of each module. The operating principle is to consider the linear influence of temperature on electrical parameters. By multiplying the temperature rise value of each module by a predetermined temperature coefficient (for equivalent impedance, the temperature coefficient represents the rate of impedance change caused by a unit temperature change; for response time, the temperature coefficient represents the rate of time delay change caused by a unit temperature change), the drift of equivalent impedance and the drift of response time are calculated respectively, thereby obtaining the thermally induced parameter drift data.

[0019] It should be added that step S23 includes the following steps: Step S231: Extract the equivalent impedance drift and response time drift based on the thermally induced parameter drift data. By analyzing the changes in equivalent impedance and response time caused by the temperature rise, the parameter drift data is obtained. Step S232: Calculate the comprehensive response time delay based on the response time delay in the parameter drift data and the module electrical characteristic difference data. The comprehensive response delay data is obtained by adding the response time drift to the original response time delay. Step S233: Perform dynamic compensation offset simulation based on comprehensive response delay data and current circulation distribution data. By simulating the impact of response delay on current distribution stability, transient response offset data is obtained.

[0020] In this embodiment of the invention, equivalent impedance drift and response time drift are extracted based on thermally induced parameter drift data. The operating principle is to analyze the linear influence of temperature rise on electrical parameters, separating the changes in equivalent impedance and response time from the thermally induced parameter drift data. Specifically, by identifying the product relationship between the temperature rise data of each compensation module and a predefined temperature coefficient (representing the rate of parameter change caused by a unit temperature change), the absolute changes in equivalent impedance and response time are calculated respectively, thus obtaining the parameter drift data. Subsequently, a comprehensive response time delay calculation is performed based on the response time delay in the parameter drift data and the module electrical characteristic difference data. The operating principle is to combine the response time of each module... The inter-wave drift (i.e., the change in response time caused by temperature) is algebraically added to the original response time delay of the module to obtain the total response time delay considering the thermal effect, thus generating comprehensive response delay data. Finally, dynamic compensation misalignment simulation is performed based on the comprehensive response delay data and current circulation distribution data. The operating principle is to construct a time-domain model of the control system to simulate the dynamic behavior of the current distribution loop under the condition of response delay. Specifically, the comprehensive response delay data is input as a system delay parameter into the current circulation model to analyze the impact of delay on the transient response of current regulation, including calculating stability indicators such as current overshoot and settling time, thereby quantifying the degree of misalignment in the dynamic compensation process and obtaining transient response misalignment data.

[0021] It should be added that step S24 includes the following steps: Step S241: Calculate the circulation deviation factor based on the current circulation distribution data. Obtain the circulation deviation factor data by calculating the average deviation between the current distribution ratio and the ideal ratio. Step S242: Evaluate the impact of parameter drift based on thermally induced parameter drift data and circulation deviation factor data. By analyzing the coupling relationship between parameter drift and circulation deviation, obtain parameter drift impact data. Step S243: Based on transient response misalignment data, circulation deviation factor data, and parameter drift influence data, misalignment influence weights are assigned. By comprehensively analyzing the interaction between transient misalignment, circulation deviation, and parameter drift, misalignment influence weight data are obtained. Step S244: Perform a comprehensive calculation of the compensation current distribution deviation based on the circulating current deviation factor data, parameter drift influence data, and offset influence weight data. Obtain the compensation current distribution deviation data by weighted fusion of the circulating current deviation factor, parameter drift influence, and offset influence weight.

[0022] Step S243 further includes the following steps: Step S2431: Calculate the imbalance severity index based on transient response imbalance data. By analyzing the proportional relationship between the transient response imbalance value and the baseline imbalance threshold, the imbalance severity index data is obtained. Step S2432: Calculate the impact of imbalance-circulation coupling based on imbalance severity index data and circulation deviation factor data. By analyzing the correlation between imbalance severity and circulation deviation, obtain the imbalance-circulation coupling impact data. Step S2433: Calculate the comprehensive impact factor based on the offset-circulation coupling effect data and parameter drift effect data. Obtain the comprehensive impact factor data by integrating the interaction between the offset-circulation coupling effect and the parameter drift effect. Step S2434: Based on the imbalance severity index data, imbalance-circulation coupling effect data, and comprehensive influence factor data, the imbalance influence weight is allocated. By comprehensively analyzing the relationship between imbalance severity, coupling effect, and comprehensive influence factor, the imbalance influence weight data is obtained.

[0023] In this embodiment of the invention, the circulating current deviation factor is calculated based on the circulating current distribution data. The operating principle is to statistically analyze the absolute deviation values ​​between the current distribution ratio of all compensation modules and the ideal current distribution ratio, and calculate the arithmetic mean of these deviation values ​​to obtain the circulating current deviation factor data. Subsequently, the parameter drift influence is evaluated based on the thermally induced parameter drift data and the circulating current deviation factor data. The operating principle is to analyze the synergistic effect between the equivalent impedance drift and response time drift of each compensation module and the circulating current deviation factor. Specifically, the parameter drift of each module (including impedance change value and response time change value) is multiplied by the circulating current deviation factor, and then the average value of the product results of all modules is calculated to obtain the parameter drift influence data. Next, the imbalance impact weights are allocated based on transient response imbalance data, circulation deviation factor data, and parameter drift impact data. First, the imbalance severity index is calculated based on the transient response imbalance data. This is done by comparing the transient response imbalance value of each module with the system's preset baseline imbalance threshold, calculating the ratio of the imbalance value to the threshold, and averaging the ratios of all modules to obtain the imbalance severity index data. Then, the imbalance-circulation coupling impact is calculated based on the imbalance severity index data and the circulation deviation factor data. This is done by multiplying the imbalance severity index by the circulation deviation factor and considering the degree of linear correlation between the two to obtain the imbalance-circulation coupling impact data. Next, a comprehensive impact factor is calculated based on the offset-circulation coupling effect data and parameter drift effect data. This is achieved by adding the offset-circulation coupling effect data and the parameter drift effect data, and introducing an adjustment coefficient to balance their contribution ratios. Finally, the offset impact weights are allocated based on the offset severity index data, offset-circulation coupling effect data, and comprehensive impact factor data. This is done by multiplying the offset severity index, offset-circulation coupling effect, and comprehensive impact factor by preset weight coefficients, summing the results, and normalizing the results to obtain offset impact weight data. Finally, a comprehensive calculation of the compensation current allocation deviation is performed based on the circulation deviation factor data, parameter drift effect data, and offset impact weight data. This calculation is performed by multiplying the circulation deviation factor, parameter drift effect, and offset impact weight by their corresponding dynamic weight coefficients, and then summing the three weighted results to obtain the compensation current allocation deviation data.

[0024] It should be added that step S25 includes the following steps: Step S251: Calculate the overload factor based on the compensation current distribution deviation data. By analyzing the ratio of the current distribution deviation to the rated current, obtain the overload factor data of each module. Step S252: Calculate the thermal stress accumulation index based on the overload coefficient data of each module and the heat dissipation coefficient in the basic setting parameters of the compensation module. By comprehensively considering the inverse relationship between the degree of overload and the heat dissipation capacity, the thermal stress accumulation index data is obtained. Step S253: Perform aging acceleration factor correlation analysis based on thermal stress accumulation index data. By establishing a positive correlation between thermal stress and material aging rate, aging acceleration factor data is obtained. Step S254: Based on the overload coefficient data, thermal stress accumulation index data and aging acceleration factor data of each module, perform a correlation analysis of the module aging rate. By comprehensively considering the interaction between overload, thermal stress and aging acceleration factor, obtain the module aging rate correlation data.

[0025] Step S254 further includes the following steps: Step S2541: Calculate the comprehensive stress coefficient based on the overload coefficient data and thermal stress accumulation index data of each module. By analyzing the synergistic relationship between the degree of overload and thermal stress accumulation, the comprehensive stress coefficient data is obtained. Step S2542: Calculate the aging rate index based on the comprehensive stress coefficient data and aging acceleration factor data. Obtain the aging rate index data through the product relationship between the comprehensive stress level and the aging acceleration factor. Step S2543: Based on the aging rate index data and the overload coefficient data of each module, perform aging dispersion analysis between modules. By calculating the variance relationship of the aging rate index of each module, the aging dispersion data is obtained. Step S2544: Perform module aging rate correlation analysis based on comprehensive stress coefficient data, aging rate index data and aging dispersion data. Obtain module aging rate correlation data through the interaction relationship between comprehensive stress level, aging rate and dispersion.

[0026] In this embodiment of the invention, the overload coefficient is calculated based on the compensation current distribution deviation data. The operating principle is to analyze the ratio of the current distribution deviation value of each compensation module to the rated current. Specifically, the absolute value of the current distribution deviation of each module is divided by its rated current value to obtain the overload coefficient data of each module. Subsequently, based on the overload coefficient data of each module and the heat dissipation coefficient in the basic setting parameters of the compensation module, the thermal stress accumulation index is calculated. The operating principle is to comprehensively consider the inverse relationship between overload degree and heat dissipation capacity. Specifically, the overload coefficient value of each module is divided by its heat dissipation coefficient value, and the average of the calculation results for all modules is taken to obtain the thermal stress accumulation index data. Next, based on the thermal stress accumulation index data, an aging acceleration factor correlation analysis is performed. The operating principle is to establish a positive correlation between thermal stress and material aging rate. Specifically, the thermal stress accumulation index is multiplied by a predefined material aging baseline rate coefficient to obtain the aging acceleration factor data. Then, based on the overload coefficient data of each module, the thermal stress accumulation index data, and the aging acceleration factor data, a module aging rate correlation analysis is performed. This analysis first calculates the comprehensive stress coefficient based on the overload coefficient data of each module and the thermal stress accumulation index data. The operating principle is to analyze the synergistic relationship between overload degree and thermal stress accumulation. Specifically, the overload coefficient value of each module is multiplied by the thermal stress accumulation index value, and the arithmetic mean of the product results for all modules is taken to obtain the comprehensive stress coefficient data. Subsequently, the aging rate index is calculated based on the comprehensive stress coefficient data and aging acceleration factor data. The operating principle is based on the product relationship between the comprehensive stress level and the aging acceleration factor. Specifically, the comprehensive stress coefficient value is multiplied by the aging acceleration factor value to obtain the aging rate index data, which represents the theoretical aging rate under comprehensive stress. Next, based on the aging rate index data and the overload coefficient data of each module, the aging dispersion analysis between modules is performed. The operating principle is based on calculating the variance relationship of the aging rate indices of each module. Specifically, the average value of the aging rate indices of all modules is first calculated, then the squared difference between the aging rate index of each module and the average value is calculated, and finally, the average of these squared differences is obtained to obtain the aging dispersion data. Finally, based on the comprehensive stress coefficient data, aging rate index data, and aging dispersion data, the module aging rate correlation analysis is performed. The operating principle is based on the interaction relationship between the comprehensive stress level, aging rate, and dispersion. Specifically, the comprehensive stress coefficient, aging rate index, and aging dispersion are multiplied by preset weighting coefficients, summed, and the results are normalized to obtain the module aging rate correlation data.

[0027] It should be added that step S26 includes the following steps: Step S261: Calculate the cumulative aging index based on the module aging rate correlation data. The cumulative aging index data is obtained by combining the product relationship between the aging acceleration factor of each module and the running time. Step S262: Calculate the heat accumulation effect based on the cumulative aging index data and the heat dissipation coefficient in the basic setting parameters of the compensation module. By analyzing the interaction between the degree of aging and the heat dissipation capacity, the heat accumulation effect data is obtained. Step S263: Simulate lifetime decay based on thermal accumulation effect data. By establishing a correlation model between thermal accumulation and material performance degradation, lifetime decay prediction data is obtained. Step S264: Based on the cumulative aging index data, thermal accumulation effect data and life decay prediction data, perform a correlation analysis on the impact of module life. By comprehensively considering the interaction between the aging index, thermal accumulation effect and life decay, obtain the correlation data on the impact of module life.

[0028] In this embodiment of the invention, the cumulative aging index is calculated based on the module aging rate correlation data. The operating principle is to comprehensively consider the product relationship between the aging acceleration factor of each module and its running time. Specifically, the aging acceleration factor value of each module is multiplied by the cumulative system running time, and the arithmetic mean of the product results of all modules is calculated to obtain the cumulative aging index data. Subsequently, the thermal accumulation effect is calculated based on the cumulative aging index data and the heat dissipation coefficient in the basic setting parameters of the compensation module. The operating principle is to analyze the interaction between the degree of aging and the heat dissipation capacity. Specifically, the cumulative aging index value is divided by the heat dissipation coefficient value to obtain the thermal accumulation effect data. Then, based on the thermal accumulation effect data... The lifespan degradation simulation operates by establishing a correlation model between thermal accumulation and material performance degradation. Specifically, the thermal accumulation effect value is multiplied by a predefined material lifespan degradation coefficient to obtain lifespan degradation prediction data, which represents the expected lifespan loss under the influence of thermal accumulation. Finally, based on the cumulative aging index data, thermal accumulation effect data, and lifespan degradation prediction data, a correlation analysis of the impact on module lifespan is performed. The operation principle is to comprehensively analyze the interaction between the aging index, thermal accumulation effect, and lifespan degradation. Specifically, the cumulative aging index, thermal accumulation effect, and lifespan degradation prediction values ​​are multiplied by preset weighting coefficients, summed, and the results are normalized to obtain the correlation data of the impact on module lifespan.

[0029] Compensation data output module: Based on the correlation data of module lifespan impact, control parameter space limit matching is performed. By matching the gain, limiting value and phase compensation parameters of the current sharing control loop, control parameter space limit matching data is generated. The output control planning design of the compensation module is performed based on the control parameter space limit matching data. By adjusting the PWM duty cycle, switching frequency and fan control signal of each compensation module, the output control planning data of the compensation module is obtained.

[0030] For example, the compensation data output module includes: Step S31: Calculate the control gain adjustment amount based on the module lifetime impact correlation data. By analyzing the inverse relationship between the degree of lifetime impact and the gain value, the control gain adjustment amount data is obtained. Step S32: Dynamically set the limiting threshold based on the control gain adjustment data and the correlation data of module lifetime impact. By correlating the lifetime decay degree with the output limiting value, obtain the dynamic setting data of the limiting threshold. Step S33: Optimize the phase compensation parameters based on the dynamic setting data of the limiting threshold and the control gain adjustment data. By combining the interaction between the limiting threshold and the gain adjustment, the optimized phase compensation parameter data is obtained. Step S34: Based on the control gain adjustment data, the dynamic setting data of the limiting threshold, and the optimization data of the phase compensation parameters, perform spatial limit matching of control parameters. By integrating the synergistic relationship between the gain, limiting, and phase parameters, generate spatial limit matching data of control parameters. Step S35: Perform dynamic planning of PWM duty cycle based on control parameter space limit matching data. By analyzing the correspondence between control parameters and duty cycle adjustment, obtain dynamic planning data for PWM duty cycle. Step S36: Based on the PWM duty cycle dynamic programming data and the control parameter space limit matching data, the switching frequency is adaptively adjusted. By correlating the duty cycle change with the switching frequency matching relationship, the switching frequency adaptive adjustment data is obtained. Step S37: Optimize the fan control signal based on the adaptive adjustment data of switching frequency and the correlation data of module lifespan. By analyzing the correspondence between switching frequency and heat dissipation requirements, the optimized fan control signal data is obtained. Step S38: Based on the PWM duty cycle dynamic programming data, the switching frequency adaptive adjustment data, and the fan control signal optimization data, the compensation module output control planning design is carried out. By combining the synergistic effect of duty cycle, switching frequency, and fan control, the compensation module output control planning data is obtained.

[0031] For example, step S31 includes the following steps: Step S311: Calculate the lifespan impact index based on the module lifespan impact correlation data. By analyzing the weighted sum relationship between the predicted lifespan decay value and the thermal accumulation effect value in the lifespan impact correlation data, the lifespan impact index data is obtained. Step S312: Calculate the gain adjustment factor based on the lifetime impact index data. By applying the inverse relationship between the lifetime impact index and the gain adjustment factor, the gain adjustment factor data is obtained. Step S313: Calculate the control gain adjustment amount based on the gain adjustment factor data and the reference gain value. The control gain adjustment amount data is obtained by multiplying the gain adjustment factor by the reference gain value.

[0032] In this embodiment of the invention, the lifetime impact index is calculated based on the module lifetime impact correlation data. The operating principle is to analyze the weighted sum relationship between the predicted lifetime decay value and the thermal accumulation effect value in the lifetime impact correlation data. Specifically, the predicted lifetime decay value and the thermal accumulation effect value are multiplied by their respective weight coefficients and then summed. The weight coefficients are dynamically adjusted according to the system operating status, ultimately yielding the lifetime impact index data. Subsequently, a gain adjustment factor is calculated based on the lifetime impact index data. The operating principle is to apply the inverse relationship between the lifetime impact index and the gain adjustment factor. Specifically, the benchmark gain adjustment factor is divided by the lifetime impact index value to obtain the actual gain adjustment factor data. Finally, the control gain adjustment amount is calculated based on the gain adjustment factor data and the benchmark gain value. The operating principle is to multiply the gain adjustment factor by the benchmark gain value. Specifically, the calculated gain adjustment factor value is multiplied by the system's preset benchmark gain value to obtain the final control gain adjustment amount data.

[0033] For example, step S32 includes the following steps: Step S321: Extract the degree of lifespan decay based on the module lifespan impact correlation data. By analyzing the ratio between the predicted lifespan decay value and the baseline lifespan value in the lifespan impact correlation data, the degree of lifespan decay data is obtained. Step S322: Calculate the safe operation coefficient based on the lifetime decay rate data and the control gain adjustment amount data. By comprehensively considering the interaction between the lifetime decay rate and the gain adjustment amount, the safe operation coefficient data is obtained. Step S323: Dynamically set the amplitude limit threshold based on the safety operation coefficient data and the benchmark amplitude limit threshold. The dynamic setting data of the amplitude limit threshold is obtained by multiplying the safety operation coefficient by the benchmark amplitude limit threshold.

[0034] In this embodiment of the invention, the degree of lifespan decay is extracted based on the correlation data of module lifespan impact. The operating principle is to analyze the proportional relationship between the predicted value of lifespan decay in the correlation data and the preset baseline lifespan value of the system. Specifically, the predicted value of lifespan decay of each module is divided by the corresponding baseline lifespan value to obtain the lifespan decay degree data of each module. Subsequently, a safe operation coefficient is calculated based on the lifespan decay degree data and the control gain adjustment amount data. The operating principle is to comprehensively consider the interaction between the lifespan decay degree and the gain adjustment amount. Specifically, the lifespan decay degree value of each module is multiplied by the control gain adjustment amount value, and the reciprocal is taken. Then, the arithmetic mean of the calculation results of all modules is calculated to obtain the safe operation coefficient data. This coefficient reflects the system's safe operation margin after considering lifespan decay and gain adjustment. Finally, a limit threshold is dynamically set based on the safe operation coefficient data and the baseline limit threshold. The operating principle is to multiply the safe operation coefficient by the baseline limit threshold. Specifically, the calculated safe operation coefficient value is multiplied by the preset baseline limit threshold value of the system to obtain the dynamically adjusted limit threshold setting data.

[0035] For example, step S33 includes the following steps: Step S331: Based on the dynamic setting data of the limiting threshold and the control gain adjustment data, the system stability is evaluated. By analyzing the matching degree between the change of the limiting threshold and the gain adjustment, the system stability evaluation index is obtained. Step S332: Calculate the phase margin requirement based on the system stability assessment index data. By establishing the correspondence between the stability index and the phase compensation requirement, the phase margin requirement data is obtained. Step S333: Calculate the compensation angle adjustment based on the phase margin requirement data and control gain adjustment data. By comprehensively considering the interaction between the phase margin requirement and the gain change, the compensation angle adjustment data is obtained. Step S334: Optimize the phase compensation parameters based on the compensation angle adjustment data and the reference phase compensation parameters. The optimized phase compensation parameter data is obtained by adding the compensation angle adjustment to the reference phase compensation parameters.

[0036] In this embodiment of the invention, system stability is evaluated based on dynamic setting data of the limiting threshold and control gain adjustment data. The operating principle is to analyze the matching degree between the change in the limiting threshold and the gain adjustment. Specifically, the ratio of the dynamic setting value of the limiting threshold to the control gain adjustment value of each module is calculated, and the standard deviation of the ratio results for all modules is obtained to obtain the system stability evaluation index. Subsequently, the phase margin requirement is calculated based on the system stability evaluation index data. The operating principle is to establish a correspondence between the stability index and the phase compensation requirement. Specifically, the system stability evaluation index value is multiplied by a preset phase margin conversion coefficient to obtain the phase margin. The process begins with obtaining the required data. Next, based on the phase margin requirement data and control gain adjustment data, the compensation angle adjustment is calculated. This is achieved by comprehensively considering the interaction between the phase margin requirement and gain change. Specifically, the phase margin requirement value is multiplied by the control gain adjustment value, and then divided by the reference gain value to obtain the compensation angle adjustment data. Finally, based on the compensation angle adjustment data and the reference phase compensation parameters, the phase compensation parameters are optimized. This is done by adding the compensation angle adjustment to the reference phase compensation parameters. Specifically, the calculated compensation angle adjustment value is algebraically added to the system's preset reference phase compensation parameters to obtain the optimized phase compensation parameter data.

[0037] For example, step S34 includes the following steps: Step S341: Based on the control gain adjustment data, the dynamic setting data of the limiting threshold, and the optimization data of the phase compensation parameters, evaluate the parameter synergy effect. By analyzing the mutual influence relationship between the gain adjustment, the limiting threshold, and the phase compensation parameters, obtain the parameter synergy effect data. Step S342: Calculate the control space stability boundary based on the parameter synergy effect data. Obtain the control space stability boundary data through the correspondence between parameter synergy effects and system stability boundaries. Step S343: Generate control parameter space limit matching data based on control space stability boundary data.

[0038] In this embodiment of the invention, parameter synergy effect evaluation is performed based on control gain adjustment data, dynamic setting data of the limiting threshold, and optimized data of the phase compensation parameters. The operating principle is to analyze the mutual influence between the gain adjustment, the limiting threshold, and the phase compensation parameters. Specifically, the control gain adjustment value, the dynamic setting value of the limiting threshold, and the optimized value of the phase compensation parameter for each module are multiplied, and the geometric mean of the product results for all modules is calculated to obtain the parameter synergy effect data. Subsequently, the control space stability boundary is calculated based on the parameter synergy effect data. The operating principle is to understand the correspondence between the parameter synergy effect and the system stability boundary. Specifically, the parameter synergy effect value is multiplied by a preset stability boundary conversion coefficient and then added to the baseline stability boundary value to obtain the control space stability boundary data. This data defines the parameter operating range for maintaining system stability. Finally, control parameter space limit matching data is generated based on the control space stability boundary data. The operating principle is to multiply the control space stability boundary data by the system safety factor. Specifically, the calculated control space stability boundary value is multiplied by the preset system safety factor to generate the final control parameter space limit matching data.

[0039] For example, step S35 includes the following steps: Step S351: Based on the control parameter space limit matching data, perform control parameter analysis to obtain key control parameter data, which includes gain adjustment amount, limiting threshold and phase compensation parameters; Step S352: Calculate the duty cycle adjustment based on the key control parameter data. By analyzing the correspondence between the gain adjustment, the limiting threshold, the phase compensation parameter and the duty cycle adjustment, the duty cycle adjustment data is obtained. Step S353: Perform dynamic planning of PWM duty cycle based on duty cycle adjustment data. The dynamic planning data of PWM duty cycle is obtained by adding the duty cycle adjustment amount to the reference duty cycle.

[0040] In this embodiment of the invention, control parameter parsing is performed based on control parameter space limit matching data. The operating principle involves extracting key control parameters from the control parameter space limit matching data, including gain adjustment, limiting threshold, and phase compensation parameters. Specifically, this is achieved by identifying corresponding fields and values ​​in the data and directly reading these parameter values ​​to obtain key control parameter data. Subsequently, duty cycle adjustment is calculated based on the key control parameter data. This is achieved by analyzing the correspondence between gain adjustment, limiting threshold, and phase compensation parameters and duty cycle adjustment. Specifically, the gain adjustment value, the dynamically set value of the limiting threshold, and the optimized value of the phase compensation parameter are multiplied by preset weighting coefficients, summed, and then divided by the sum of the weighting coefficients to obtain the duty cycle adjustment data. Finally, PWM duty cycle dynamic planning is performed based on the duty cycle adjustment data. This is achieved by adding the duty cycle adjustment value to a reference duty cycle. Specifically, the calculated duty cycle adjustment value is algebraically added to the system's preset reference duty cycle value to obtain PWM duty cycle dynamic planning data.

[0041] For example, step S36 includes the following steps: Step S361: Calculate the duty cycle change rate based on the PWM duty cycle dynamic programming data. By analyzing the ratio of the difference between the duty cycle dynamic programming value and the reference duty cycle value, the duty cycle change rate data is obtained. Step S362: Calculate the frequency adjustment factor based on the duty cycle change rate data and the control parameter space limit matching data. Obtain the frequency adjustment factor data by correlating the linear relationship between the duty cycle change rate and the frequency adjustment factor. Step S363: Perform adaptive adjustment of the switching frequency based on the frequency adjustment factor data and the reference switching frequency. The adaptive adjustment data of the switching frequency is obtained by multiplying the frequency adjustment factor by the reference switching frequency.

[0042] In this embodiment of the invention, the duty cycle change rate is calculated based on PWM duty cycle dynamic programming data. The operating principle is to analyze the proportional relationship between the difference between the dynamic programming value of the duty cycle and the reference duty cycle value. Specifically, the difference between the dynamic programming value of the PWM duty cycle of each module and the system-preset reference duty cycle value is calculated, and then this difference is divided by the reference duty cycle value to obtain the duty cycle change rate data. Subsequently, the frequency adjustment factor is calculated based on the duty cycle change rate data and the control parameter space limit matching data. The operating principle is to correlate the linear relationship between the duty cycle change rate and the frequency adjustment factor. Specifically, the duty cycle change rate value is multiplied by the frequency adjustment coefficient in the control parameter space limit matching data to obtain the frequency adjustment factor data. This data is used to quantify the degree of influence of duty cycle changes on the switching frequency. Finally, the switching frequency is adaptively adjusted based on the frequency adjustment factor data and the reference switching frequency. The operating principle is to multiply the frequency adjustment factor by the reference switching frequency. Specifically, the calculated frequency adjustment factor value is multiplied by the system-preset reference switching frequency to obtain the switching frequency adaptive adjustment data.

[0043] For example, step S37 includes the following steps: Step S371: Perform heat dissipation demand analysis based on the adaptive adjustment data of switching frequency. By analyzing the linear correspondence between the change of switching frequency and the heat dissipation demand, heat dissipation demand data is obtained. Step S372: Based on the heat dissipation demand data and the correlation data of module lifespan impact, conduct a heat dissipation urgency assessment. By correlating the heat accumulation effect value in the heat dissipation demand and the degree of lifespan impact, obtain the heat dissipation urgency data. Step S373: Optimize the fan control signal based on the heat dissipation urgency data. By converting the heat dissipation urgency data into the fan speed adjustment amount, the optimized fan control signal data is obtained.

[0044] In this embodiment of the invention, heat dissipation demand analysis is performed based on adaptive switching frequency adjustment data. The operating principle involves analyzing the linear correspondence between switching frequency changes and heat dissipation demand. Specifically, the difference between the adaptive switching frequency adjustment value of each compensation module and the system's preset reference switching frequency is calculated. This difference is then divided by the reference switching frequency to obtain the frequency change rate, which is multiplied by a preset heat dissipation demand coefficient to obtain heat dissipation demand data. Subsequently, heat dissipation urgency is assessed based on the heat dissipation demand data and module lifespan impact correlation data. This assessment is performed by correlating the heat dissipation demand with the heat accumulation effect value in the lifespan impact level. Specifically, the heat dissipation demand value of each module is multiplied by the heat accumulation effect value in the module lifespan impact correlation data, and then divided by the system's preset reference heat accumulation value to obtain heat dissipation urgency data. Finally, fan control signal optimization is performed based on the heat dissipation urgency data. This optimization involves converting the heat dissipation urgency data into a fan speed adjustment amount. Specifically, the heat dissipation urgency value is multiplied by a preset fan speed adjustment coefficient and added to the system's preset reference fan speed to obtain optimized fan control signal data.

[0045] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0046] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0047] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0048] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0049] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent low-voltage compensation switchgear, characterized in that, include: Electrical data acquisition module: acquires the electrical connection diagram of the intelligent low-voltage compensation cabinet and the basic setting parameters of the compensation modules, including rated current, rated voltage, impedance characteristics, heat dissipation coefficient and response time; based on the electrical connection diagram, it performs electrical characteristic difference analysis between compensation modules, and obtains module electrical characteristic difference data by calculating the equivalent impedance deviation, phase angle difference and response time delay of each compensation module; Compensation Module Analysis Module: Based on the basic setting parameters of the compensation module, the module performs a simulation analysis of the difference in electrical characteristics of the module to obtain the compensation current distribution deviation data by simulating the current circulation, thermal parameter drift and transient response misalignment when multiple modules are running in parallel. Based on the compensation current distribution deviation data, the module lifespan impact correlation is mined. By correlating the current unevenness with the module aging rate and combining the thermal accumulation model to predict lifespan decay, the module lifespan impact correlation data is obtained. Compensation data output module: Based on the correlation data of module lifespan impact, control parameter space limit matching is performed. By matching the gain, limiting value and phase compensation parameters of the current sharing control loop, control parameter space limit matching data is generated. The output control planning design of the compensation module is performed based on the control parameter space limit matching data. By adjusting the PWM duty cycle, switching frequency and fan control signal of each compensation module, the output control planning data of the compensation module is obtained.

2. The intelligent low-voltage compensation cabinet according to claim 1, characterized in that, The working logic of the electrical data acquisition module is as follows: Obtain the electrical connection diagram and basic setting parameters of the intelligent low-voltage compensation cabinet, including rated current, rated voltage, impedance characteristics, heat dissipation coefficient and response time. The electrical connection diagram is vectorized to generate an electrical connection vector diagram. The electrical connection vector diagram is analyzed to obtain the connection relationship data of the compensation modules. The connection relationship data of the compensation modules includes the installation position of each compensation module in the cabinet, the electrical connection topology and parallel node information. Based on the connection relationship data and basic setting parameters of the compensation modules, the electrical characteristic differences between the compensation modules are analyzed. The equivalent impedance deviation, phase angle difference, and response time delay of each compensation module are calculated to obtain the electrical characteristic difference data of the modules. Among them, the equivalent impedance deviation is calculated by comparing the absolute difference between the equivalent impedance value of each compensation module and the average equivalent impedance value; the phase angle difference is obtained by calculating the difference between the phase angle between the output current and voltage of each compensation module and the reference phase angle; and the response time delay is obtained by calculating the difference between the response time of each compensation module and the minimum response time.

3. A smart low-voltage compensation cabinet according to claim 1, characterized in that, The implementation logic of the compensation module analysis module includes: Step S21: Perform a multi-module parallel current circulation simulation based on the basic setting parameters of the compensation module and the difference data of the module electrical characteristics. By calculating the deviation between the current distribution ratio of each compensation module and the ideal ratio, the current circulation distribution data is obtained. Step S22: Based on the current circulation distribution data and the heat dissipation coefficient in the basic setting parameters of the compensation module, perform thermal parameter drift simulation. By simulating the temperature rise and parameter changes caused by uneven current, obtain thermal parameter drift data. Step S23: Perform transient response misalignment simulation based on thermally induced parameter drift data, module electrical characteristic difference data, and current circulation distribution data. By analyzing the impact of parameter drift and response time delay on dynamic compensation, transient response misalignment data is obtained. Step S24: Based on the current circulation distribution data, thermally induced parameter drift data and transient response offset data, perform a comprehensive calculation of the compensation current distribution deviation. By weighted fusion of circulation, drift and offset factors, the compensation current distribution deviation data is obtained. Step S25: Perform module aging rate correlation analysis based on compensation current distribution deviation data. By correlating current deviation with aging acceleration factor, obtain module aging rate correlation data. Step S26: Based on the module aging rate correlation data and the heat dissipation coefficient in the basic setting parameters of the compensation module, perform thermal accumulation model lifetime prediction. By simulating the decay effect of long-term thermal accumulation on lifetime, obtain the correlation data of module lifetime impact.

4. An intelligent low-voltage compensation switchboard according to claim 3, characterized in that, Step S21 includes the following steps: Step S211: Calculate the equivalent impedance deviation of each compensation module based on the module electrical characteristic difference data. By comparing the absolute difference between the equivalent impedance value of each module and the average equivalent impedance value, obtain the equivalent impedance deviation data of each module. Step S212: Simulate the current distribution of the parallel circuit based on the equivalent impedance deviation data of each module and the rated current in the basic setting parameters of the compensation module. By simulating the current output ratio of each module in the parallel circuit, obtain the actual current distribution ratio data. Step S213: Calculate the ideal current distribution ratio based on the rated current and rated voltage in the basic setting parameters of the compensation module. Obtain the ideal current distribution ratio data by distributing the total compensation current proportionally according to the rated capacity of the module. Step S214: Perform current distribution deviation analysis based on actual current distribution ratio data and ideal current distribution ratio data. By calculating the difference between the actual ratio and the ideal ratio of each module, obtain the current circulation distribution data.

5. An intelligent low-voltage compensation switchgear according to claim 3, characterized in that, Step S22 includes the following steps: Step S221: Calculate the actual current value of each module based on the current circulation distribution data and the rated current in the basic setting parameters of the compensation module. The actual current data of each module is obtained by adding the current distribution ratio deviation to the ideal current ratio and multiplying it by the total rated current. Step S222: Calculate heat generation based on the equivalent impedance in the actual current data and electrical characteristic difference data of each module. Obtain the heat generation data of each module by calculating the product relationship between the square of the current and the equivalent impedance value of each module. Step S223: Based on the heat generation data of each module and the heat dissipation coefficient in the basic setting parameters of the compensation module, perform temperature rise simulation, calculate steady-state temperature value by simulating the balance relationship between heat generation and heat dissipation coefficient, and obtain temperature rise data of each module; Step S224: Perform parameter drift simulation based on the temperature rise data of each module. By simulating the linear influence of temperature rise on equivalent impedance and response time, obtain thermally induced parameter drift data.

6. A smart low-voltage compensation switchboard according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Calculate the circulation deviation factor based on the current circulation distribution data. Obtain the circulation deviation factor data by calculating the average deviation between the current distribution ratio and the ideal ratio. Step S242: Evaluate the impact of parameter drift based on thermally induced parameter drift data and circulation deviation factor data. By analyzing the coupling relationship between parameter drift and circulation deviation, obtain parameter drift impact data. Step S243: Based on transient response misalignment data, circulation deviation factor data, and parameter drift influence data, misalignment influence weights are assigned. By comprehensively analyzing the interaction between transient misalignment, circulation deviation, and parameter drift, misalignment influence weight data are obtained. Step S244: Perform a comprehensive calculation of the compensation current distribution deviation based on the circulating current deviation factor data, parameter drift influence data, and offset influence weight data. Obtain the compensation current distribution deviation data by weighted fusion of the circulating current deviation factor, parameter drift influence, and offset influence weight.

7. An intelligent low-voltage compensation switchgear according to claim 6, characterized in that, Step S243 further includes the following steps: Step S2431: Calculate the imbalance severity index based on transient response imbalance data. By analyzing the proportional relationship between the transient response imbalance value and the baseline imbalance threshold, the imbalance severity index data is obtained. Step S2432: Calculate the impact of imbalance-circulation coupling based on imbalance severity index data and circulation deviation factor data. By analyzing the correlation between imbalance severity and circulation deviation, obtain the imbalance-circulation coupling impact data. Step S2433: Calculate the comprehensive impact factor based on the offset-circulation coupling effect data and parameter drift effect data. Obtain the comprehensive impact factor data by integrating the interaction between the offset-circulation coupling effect and the parameter drift effect. Step S2434: Based on the imbalance severity index data, imbalance-circulation coupling effect data, and comprehensive influence factor data, the imbalance influence weight is allocated. By comprehensively analyzing the relationship between imbalance severity, coupling effect, and comprehensive influence factor, the imbalance influence weight data is obtained.

8. An intelligent low-voltage compensation cabinet according to claim 1, characterized in that, The compensation data output module includes: Step S31: Calculate the control gain adjustment amount based on the module lifetime impact correlation data. By analyzing the inverse relationship between the degree of lifetime impact and the gain value, the control gain adjustment amount data is obtained. Step S32: Dynamically set the limiting threshold based on the control gain adjustment data and the correlation data of module lifetime impact. By correlating the lifetime decay degree with the output limiting value, obtain the dynamic setting data of the limiting threshold. Step S33: Optimize the phase compensation parameters based on the dynamic setting data of the limiting threshold and the control gain adjustment data. By combining the interaction between the limiting threshold and the gain adjustment, the optimized phase compensation parameter data is obtained. Step S34: Based on the control gain adjustment data, the dynamic setting data of the limiting threshold, and the optimization data of the phase compensation parameters, perform spatial limit matching of control parameters. By integrating the synergistic relationship between the gain, limiting, and phase parameters, generate spatial limit matching data of control parameters. Step S35: Perform dynamic planning of PWM duty cycle based on control parameter space limit matching data. By analyzing the correspondence between control parameters and duty cycle adjustment, obtain dynamic planning data for PWM duty cycle. Step S36: Based on the PWM duty cycle dynamic programming data and the control parameter space limit matching data, the switching frequency is adaptively adjusted. By correlating the duty cycle change with the switching frequency matching relationship, the switching frequency adaptive adjustment data is obtained. Step S37: Optimize the fan control signal based on the adaptive adjustment data of switching frequency and the correlation data of module lifespan. By analyzing the correspondence between switching frequency and heat dissipation requirements, the optimized fan control signal data is obtained. Step S38: Based on the PWM duty cycle dynamic programming data, the switching frequency adaptive adjustment data, and the fan control signal optimization data, the compensation module output control planning design is carried out. By combining the synergistic effect of duty cycle, switching frequency, and fan control, the compensation module output control planning data is obtained.

9. A smart low-voltage compensation switchboard according to claim 8, characterized in that, Step S33 includes the following steps: Step S331: Based on the dynamic setting data of the limiting threshold and the control gain adjustment data, the system stability is evaluated. By analyzing the matching degree between the change of the limiting threshold and the gain adjustment, the system stability evaluation index is obtained. Step S332: Calculate the phase margin requirement based on the system stability assessment index data. By establishing the correspondence between the stability index and the phase compensation requirement, the phase margin requirement data is obtained. Step S333: Calculate the compensation angle adjustment based on the phase margin requirement data and control gain adjustment data. By comprehensively considering the interaction between the phase margin requirement and the gain change, the compensation angle adjustment data is obtained. Step S334: Optimize the phase compensation parameters based on the compensation angle adjustment data and the reference phase compensation parameters. The optimized phase compensation parameter data is obtained by adding the compensation angle adjustment to the reference phase compensation parameters.

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