Intelligent optimization control method and system for low-voltage power supply pile

Abstract

The invention discloses an intelligent optimization control method and system for a low-voltage power supply pile, and relates to the technical field of intelligent optimization of power supply piles, the system monitors operation data in the operation process of the power supply pile in real time by installing multiple types of sensors, high-precision timestamps are synchronously input, and a polarization response data set and a disturbance data set are constructed after preprocessing. The method comprises the following steps: calculating a polarization response control index fpj, performing polarization response evaluation on the polarization response control index fpj and a set insulation polarization risk threshold A, and calculating an asymmetric disturbance energy index asy when the polarization response evaluation is that polarization backlog exists; summarizing and calculating to obtain a comprehensive dynamic regulation and control index vcm, performing electric field disturbance risk assessment on the comprehensive dynamic regulation and control index vcm and a set disturbance asymmetric tolerance threshold B, and executing a regulation and control instruction according to an assessment result. Through real-time data acquisition and evaluation, the system realizes synchronous sensing and combined regulation and control of polarization abnormity and disturbance trend in the starting process of the power supply pile, and the electric field control precision and insulation stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent optimization of power supply piles, in particular to a low-voltage power supply pile intelligent optimization control method and system. BACKGROUND

[0002] With the large-scale popularization of new energy vehicles, low-voltage power supply piles gradually become one of the important infrastructures in urban electric transportation systems. Compared with high-voltage fast charging piles, low-voltage power supply piles have the advantages of flexible layout, low cost, high safety, etc., and are widely used in residential areas, office areas and shared travel scenes. However, under the conditions of high humidity environment or frequent load fluctuation, the starting stability and system adaptive adjustment capability of the traditional low-voltage power supply pile still have obvious shortcomings. In order to realize higher dimension of charging safety and efficiency improvement, the concept of "intelligent optimization control" emerges as the times require, aiming to use multi-source sensors to collect power supply state data in real time and build dynamic control logic. The core of the system focuses on the key sensitive area in the power supply link, that is, the insulation layer of the power supply cable, whose dielectric properties, polarization behavior and leakage risk directly affect whether the starting process of the whole system is smooth and safe.

[0003] The existing low-voltage power supply system mainly uses static design parameters for control, and ignores that in the actual running environment, the insulation layer of the power supply cable will be affected by environmental humidity, temperature change and electromagnetic disturbance together, resulting in dynamic changes in its dielectric properties. Especially in the long-term high humidity and plum rain season, the surface of the insulation layer is prone to "polarization backlog" phenomenon, which shows abnormal states such as slow rise of leakage current and unbalanced electric field distribution; at the same time, due to the failure to effectively monitor the magnetic disturbance and current density change, the system is not easy to identify the potential "disturbance concentration area", so that it is not easy to intervene and adjust the control strategy in time. This control mechanism driven by "experience threshold" and "fixed logic" lacks the linkage perception ability to the changes of multiple physical fields, thereby reducing the response efficiency to the electric field disturbance risk and the evolution trend of insulation fatigue. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a low-voltage power supply pile intelligent optimization control method and system, which solves the problems in the background art.

[0005] In order to achieve the above purpose, the present application is realized by the following technical scheme: a low-voltage power supply pile intelligent optimization control system, comprising a synchronous sensing module, a polarization evaluation module, a disturbance energy analysis module and a comprehensive regulation and control module;

[0006] The synchronous sensing module is used for monitoring the running data of the power supply pile in real time according to the installed sensor group, and pre-processing the running data to obtain a polarization response data group and a disturbance data group;

[0007] The polarization evaluation module is configured to calculate the polarization response control index fpj according to the polarization response data set, and set the insulation polarization risk threshold A to evaluate the polarization response according to the polarization response control index fpj;

[0008] The disturbance energy analysis module is configured to calculate the asymmetric disturbance energy index asy according to the disturbance data set when the polarization response evaluation indicates that there is polarization backlog;

[0009] The comprehensive regulation module is configured to calculate the comprehensive dynamic regulation index vcm according to the polarization response control index fpj and the asymmetric disturbance energy index asy, set the disturbance asymmetric tolerance threshold B to evaluate the electric field disturbance risk according to the comprehensive dynamic regulation index vcm, and execute the regulation instruction according to the evaluation result.

[0010] Preferably, the synchronous perception module comprises a data perception unit and a data processing unit.

[0011] The data perception unit is configured to monitor the operation data of the low-voltage power supply pile in real time according to the sensor set installed inside the low-voltage power supply pile and on the surface of the power cable insulation layer.

[0012] The sampling period of the sensor set is controlled by the synchronous clock generated by the same RTC, and the monitored operation data is punched into a μs-level time stamp.

[0013] The sensor set comprises an IR thermopile sensor, a flexible patch current sensor, a humidity sensor, a multi-point array Hall sensor, and a micro current density sensor array.

[0014] The IR thermopile sensor is attached to the outer wall of the cable insulation layer in a spiral manner to monitor the cable surface temperature ts in real time, indicating the thermal response state of the insulation material and correlating with the polarization response rate.

[0015] The flexible patch current sensor is embedded in the insulation layer outside the wrapping belt to form a contact coupling loop, which monitors the insulation layer surface leakage current il in real time, indicating the electric field leakage condition of the insulation layer under humid conditions.

[0016] The humidity sensor installed near the power supply pile monitors the relative humidity rh of the environment in real time, which directly affects the polarization degree and the moisture absorption rate of the medium.

[0017] According to the spatial distribution characteristics of the power supply channel inside the power supply pile, the multi-point array Hall sensor is installed in sequence to monitor the magnetic induction intensity cg and the vertical magnetic field intensity bz in real time, which respectively represent the local magnetic disturbance during transient inrush and the inrush local density peak value in the initial stage of power supply anomaly.

[0018] A miniature current density sensor array is sequentially installed around the copper core of the cable to monitor the local current density dm of the conductor in real time, which represents the actual current density flowing inside the conductor per unit cross-sectional area.

[0019] Preferably, the data processing unit is used to preprocess the real-time acquired operating data to obtain polarization response data sets and disturbance data sets;

[0020] The preprocessing includes dimensionless processing, noise reduction, missing value processing, and outlier processing.

[0021] The dimensionless processing uses Z-Score standardization to perform standard transformation on the multiphysics data. The denoising uses multidimensional filtering technology to suppress noise in the running data, decompose and eliminate the noise influence in the data. Missing value processing uses the mean imputation method to fill in the missing values ​​in the dataset. Outlier processing uses the interquartile range method to detect and process outliers in the collected running data.

[0022] The polarization response data set includes cable surface temperature ts, insulation surface leakage current il, and ambient relative humidity rh;

[0023] The disturbance data set includes magnetic induction intensity cg, vertical magnetic field intensity bz, and conductor local current density dm.

[0024] Preferably, the polarization assessment module includes a polarization response analysis unit and a polarization risk assessment unit;

[0025] The polarization response analysis unit is used to calculate based on the acquired polarization response data set to obtain the polarization response control index fpj, which is used to determine whether the insulation layer has polarization abnormalities due to humidity and aging. The specific formula is as follows:

[0026] ;

[0027] In the formula, ln represents the logarithmic function, dt represents the time integral quantity, and ts(t), il(t), and rh(t) represent the cable surface temperature, insulation surface leakage current, and ambient relative humidity at time t, respectively.

[0028] Preferably, the polarization risk assessment unit is used to preset an insulation polarization risk threshold A by the user based on the limit requirements of insulation performance under the standard state of the power supply pile, and to assess the polarization response with the real-time acquired polarization response control index fpj. The specific assessment scheme is as follows:

[0029] When the polarization response control index fpj ≤ insulation polarization risk threshold A, it indicates that the insulation layer has not formed a hysteretic polarization effect and maintains the normal start-up mode;

[0030] When the polarization response control index fpj > insulation polarization risk threshold A, it indicates that there is polarization accumulation in the insulation layer, and at this time, disturbance distribution analysis is performed.

[0031] Preferably, the disturbance energy analysis module is used to perform disturbance distribution analysis when the polarization response is assessed as having polarization accumulation;

[0032] Disturbance distribution analysis is used to calculate based on disturbance data sets to obtain the asymmetric disturbance energy index asy, which is used to analyze whether the power supply system has formed a disturbance concentration area under the dual disturbance of magnetic field and current signal. The specific formula is as follows;

[0033] ;

[0034] In the formula, This represents the gradient of magnetic induction intensity along the direction of the cable.

[0035] Preferably, the integrated control module includes a dynamic control analysis unit and an evaluation control unit;

[0036] The dynamic control analysis unit is used to summarize and calculate the comprehensive dynamic control index vcm based on the polarization response control index fpj and the asymmetric disturbance energy index asy. This index is used to couple the polarization anomaly with the disturbance trend synchronously to generate a precise dynamic voltage regulation control function, thereby realizing intelligent soft compensation adjustment of the startup process. The specific formula is as follows.

[0037] ;

[0038] In the formula, fpj(t) and asy(t) represent the polarization response control exponent and the asymmetric disturbance energy exponent at time t, respectively; T0 represents the user-defined adjustment period; and sin represents the sine function. dt represents pi, with a value to two decimal places, and dt represents the time calculus.

[0039] Preferably, the evaluation and control unit includes a disturbance evaluation unit and a control command unit;

[0040] The disturbance assessment unit is used to collect all historical comprehensive dynamic control indices vcm, calculate the average value, set a disturbance asymmetry tolerance threshold B based on the average value, and then perform an electric field disturbance risk assessment with the real-time acquired comprehensive dynamic control index vcm. The specific assessment scheme is as follows:

[0041] When the comprehensive dynamic control index vcm ≤ the disturbance asymmetry tolerance threshold B, it means that the electric field disturbance is within a safe range. At this time, the gradual increase control command is executed for regulation.

[0042] When the comprehensive dynamic control index vcm > the disturbance asymmetry tolerance threshold B, it indicates that the electric field disturbance is unbalanced. At this time, the dual-mode control command of strong soft start and pulse regulation compensation is executed for regulation.

[0043] Preferably, the control instruction unit is used to execute control instructions after the electric field disturbance risk assessment, as follows;

[0044] When the gradual ascent control command is executed, the control behavior of the command includes:

[0045] The duty cycle adjustment module of the PWM controller adjusts the duty cycle rise gradient control value from the original setting to 130% of the original value, thereby extending the voltage rise time from 200ms to 260ms.

[0046] Power MOS current limiting module: By controlling the rise rate of the MOS gate voltage, the peak startup current is limited to 80% of the original set value;

[0047] RC filter control module: Adjusts the filter bandwidth to reduce the output voltage rise slope by 25%;

[0048] When executing the dual-mode control command of strong soft start and pulse regulation compensation, the control behavior of the command includes:

[0049] PWM timer slope limiting module: extends the duty cycle growth period to the original setting of 180%, increasing the total voltage rise time to 360ms;

[0050] Output voltage redundancy control module: controls the redundancy fluctuation band of the target voltage by dynamically adjusting the reference source to ±7%;

[0051] Periodic perturbation modulation module: Through the Dither control strategy, a micro-perturbation of ±0.2ms is generated in the PWM period;

[0052] High duty cycle limiting module: forces the duty cycle to not exceed 85% of the rated value.

[0053] A method for intelligent optimization control of low-voltage power supply piles includes the following steps:

[0054] S1. Based on the installed sensor group, monitor the operation data of the power supply pile in real time, and preprocess the operation data to obtain polarization response data group and disturbance data group;

[0055] S2. Calculate the polarization response control index fpj based on the polarization response data set, and set the insulation polarization risk threshold A and the polarization response control index fpj to evaluate the polarization response.

[0056] S3. When the polarization response assessment indicates the presence of polarization accumulation, the asymmetric perturbation energy index asy is obtained by calculation based on the perturbation data set.

[0057] S4. Based on the polarization response control index fpj and the asymmetric disturbance energy index asy, the comprehensive dynamic control index vcm is obtained. The disturbance asymmetric tolerance threshold B and the comprehensive dynamic control index vcm are used to conduct an electric field disturbance risk assessment. Then, the control command is executed based on the assessment results.

[0058] This invention provides an intelligent optimization control method and system for low-voltage power supply piles. It has the following beneficial effects:

[0059] (1) The synchronous sensing module of this system realizes real-time, synchronous, high-resolution sensing and timestamp labeling of multiple physical quantities in the operating environment of the power supply pile through a high-precision sensor group. This module can effectively capture early signs of polarization behavior and changes in disturbance field strength, establish a complete data acquisition closed loop, and obtain polarization response data group and disturbance data group through data preprocessing process to ensure the quality of input data. It provides stable and reliable polarization response data group and disturbance data group for subsequent modules, and completes the standardization and computability of sensing data.

[0060] (2) The polarization assessment module and the disturbance energy analysis module of this system focus on the independent identification and indexed assessment of insulation polarization anomalies and electric field disturbance anomalies, respectively. The polarization assessment module calculates the polarization response control index fpj by constructing a nonlinear integral function from the coupled data of temperature, current and humidity using the polarization response data set. This accurately identifies the polarization accumulation trend induced by aging or humidity and performs polarization response assessment with the set insulation polarization risk threshold A, thus achieving pre-identification of polarization risks. The disturbance energy analysis module calculates the asymmetric disturbance energy index asy based on the joint analysis of magnetic field gradient and current density. This effectively identifies potential spatial disturbance accumulation areas during power supply, completes the spatial directional quantitative assessment of electromagnetic disturbance risks, and improves the system's ability to identify dynamic abnormal areas.

[0061] (3) The system's integrated control module constructs a control mechanism based on the coupling of time window integral and nonlinear response function. The polarization response control index fpj and the asymmetric disturbance energy index asy are aggregated and calculated to obtain the comprehensive dynamic control index vcm. The disturbance asymmetric tolerance threshold B and the comprehensive dynamic control index vcm are set to conduct electric field disturbance risk assessment. The polarization and disturbance information are integrated and judged in real time to achieve soft compensation control of the start-up process of gradual rise, enhancement, and gradual shutdown. This mechanism not only effectively reduces the probability of insulation breakdown caused by peak voltage, but also enhances the adaptability and robustness of the power supply pile in complex loads and high humidity environments. Overall, it achieves the dual guarantee goal of stability and insulation response safety during the voltage rise process, providing a new path for improving the intelligence and reliability of urban low-voltage power distribution systems. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the intelligent optimization control system for low-voltage power supply piles according to the present invention;

[0063] Figure 2 This is a schematic diagram illustrating the steps of an intelligent optimization control method for low-voltage power supply piles according to the present invention.

[0064] Figure 3 This is a schematic diagram of the operating principle of an intelligent optimization control system for low-voltage power supply piles according to the present invention.

[0065] Figure 4 This is a line graph diagram illustrating the electric field disturbance risk assessment of this invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] Please see Figure 1 This invention provides an intelligent optimization control system for low-voltage power supply piles. To achieve the above objectives, this invention is implemented through the following technical solutions: including a synchronous sensing module, a polarization evaluation module, a disturbance energy analysis module, and a comprehensive control module.

[0069] The synchronous sensing module is used to monitor the operation data of the power supply pile in real time based on the installed sensor group, and to preprocess the operation data to obtain polarization response data group and disturbance data group;

[0070] The polarization assessment module is used to calculate based on the polarization response data set, obtain the polarization response control index fpj, and set the insulation polarization risk threshold A and the polarization response control index fpj to perform polarization response assessment.

[0071] The perturbation energy analysis module is used to calculate and obtain the asymmetric perturbation energy index asy based on the perturbation data set when the polarization response assessment indicates the presence of polarization accumulation.

[0072] The integrated control module is used to calculate the integrated dynamic control index vcm by summarizing the polarization response control index fpj and the asymmetric disturbance energy index asy. It also sets the disturbance asymmetric tolerance threshold B and the integrated dynamic control index vcm to conduct electric field disturbance risk assessment, and then executes control commands based on the assessment results.

[0073] In this embodiment, the synchronous sensing module realizes full dynamic monitoring and data structure preprocessing of the power supply pile's operating status, effectively dividing the raw sensor data into polarization response data groups and disturbance data groups, thus building a data foundation for subsequent evaluation calculations. The installed sensors enable collaborative acquisition of the power supply pile's operating data, greatly enhancing the system's ability to recognize the state under multi-physical coupling. Compared to traditional systems that rely solely on voltage and current thresholds for single-point alarms, this system has a wider sensing range and higher timing accuracy, laying a solid foundation for dynamic control. The polarization evaluation module calculates the polarization response control index fpj based on the polarization response data group and compares it with a preset insulation polarization risk threshold A. When the polarization response control index fpj exceeds the insulation polarization risk threshold A, it is considered that there is a risk of insulation polarization accumulation, triggering the operation of the disturbance energy analysis module; the latter further calculates the asymmetric disturbance energy index asy based on the disturbance data group to determine whether there is an electric field disturbance accumulation region. When the polarization response control index fpj exceeds the insulation polarization risk threshold A, the linkage judgment mechanism of the asymmetric disturbance energy index asy is activated. This not only assesses the trend anomaly of insulation polarization but also simultaneously quantifies the unevenness of disturbance distribution. This solves the shortcomings of traditional systems that "misjudge disturbances as normal fluctuations" and "ignore polarization," achieving a comprehensive risk identification driven by both polarization and disturbance behaviors. In the integrated control module, the system integrates the real-time calculated polarization response control index fpj and the asymmetric disturbance energy index asy to construct a comprehensive dynamic control index vcm, which is then compared with the disturbance asymmetric tolerance threshold B. When the comprehensive dynamic control index vcm is less than or equal to the disturbance asymmetric tolerance threshold B, a gradual increase control command is executed; when the comprehensive dynamic control index vcm is greater than the disturbance asymmetric tolerance threshold B, a dual-mode control mechanism of strong soft start and pulse regulation compensation is activated, implementing precise measures at multiple levels, including time function adjustment, slope limiting, and voltage redundancy control. Compared with the rigid strategies of fixed periods and fixed thresholds in the past, this mechanism has achieved a qualitative leap in time response, adaptive compensation and energy injection control. It greatly improves the self-regulation capability and voltage stability of power supply piles under damp aging and electromagnetic disturbances, and provides a more forward-looking risk identification and flexible control scheme for intelligent power systems.

[0074] Example 2

[0075] Please refer to Figure 1 Specifically: the synchronous sensing module includes a data sensing unit and a data processing unit;

[0076] The data sensing unit is used to monitor the operating data of the low-voltage power supply pile in real time based on the sensor group installed inside the low-voltage power supply pile and on the surface of the power supply cable insulation layer.

[0077] The sampling period of the sensor group is controlled by a synchronous clock generated by the same RTC, and the monitored operating data is stamped with a μs-level timestamp.

[0078] The sensor group includes an IR thermopile sensor, a flexible patch current sensor, a humidity sensor, a multi-point array Hall sensor, and a miniature current density sensor array.

[0079] An IR thermopile sensor is spirally attached to the outer wall of the cable insulation layer to monitor the cable surface temperature ts in real time. The polarization instability caused by the effect of temperature increase on the dielectric constant of the material is monitored, which indicates the thermal response state of the insulation material and correlates the polarization response rate.

[0080] A flexible patch current sensor is embedded in the outer wrapping tape of the insulation layer to form a contact coupling circuit. The leakage current il on the surface of the insulation layer is monitored in real time, which indicates the electric field leakage of the insulation layer under humid conditions and monitors the weak external current leakage caused by incomplete insulation polarization effect.

[0081] The relative humidity (rh) of the environment is monitored in real time by a humidity sensor installed near the power supply pile, which directly affects the polarization degree and the moisture absorption rate of the medium.

[0082] Based on the spatial distribution characteristics of the power supply channels inside the power supply pile, multi-point array Hall sensors are installed in sequence to monitor the magnetic induction intensity cg and the vertical magnetic field intensity bz in real time, which respectively represent the local magnetic disturbance during transient inrush and the peak value of the local density of the inrush during the initial stage of power supply anomaly.

[0083] A miniature current density sensor array is sequentially installed around the copper core of the cable to monitor the local current density dm of the conductor in real time, which represents the actual current density flowing inside the conductor per unit cross-sectional area.

[0084] The data processing unit is used to preprocess the real-time acquired operating data to obtain polarization response data sets and disturbance data sets;

[0085] The preprocessing includes dimensionless processing, noise reduction, missing value processing, and outlier processing.

[0086] The dimensionless processing uses Z-Score standardization to perform standard transformation on the multiphysics data. The denoising uses multidimensional filtering technology to suppress noise in the running data, decompose and eliminate the noise influence in the data. Missing value processing uses the mean imputation method to fill in the missing values ​​in the dataset. Outlier processing uses the interquartile range method to detect and process outliers in the collected running data.

[0087] The polarization response data set includes cable surface temperature ts, insulation surface leakage current il, and ambient relative humidity rh;

[0088] The disturbance data set includes magnetic induction intensity cg, vertical magnetic field intensity bz, and conductor local current density dm.

[0089] In this embodiment, by integrating an IR thermopile sensor, a flexible patch current sensor, a humidity sensor, a multi-point array Hall sensor, and a micro current density sensor array inside the low-voltage power supply pile and on its cable structure, and controlling its sampling rhythm with a unified RTC clock, high-precision time-stamped data acquisition with μs-level timestamps is achieved. The data sensing unit comprehensively monitors the operating data of the low-voltage power supply pile in the power supply system. The data processing unit uses a multi-step processing mechanism, including Z-Score standardization, multi-dimensional filtering, mean filling, and interquartile range method, to structure the raw data into polarization response data groups and disturbance data groups. This implementation not only achieves accurate data separation and extraction of insulation polarization dynamics and magnetoelectric disturbance behavior, but also significantly enhances the ability to identify early risks under complex operating conditions. Compared with traditional single-variable sensing systems, it greatly improves data accuracy, recognition breadth, and system response foresight, providing accurate, stable, and traceable data support for subsequent index calculation and control strategies, and significantly improving the intelligence and stability of power supply pile operation.

[0090] Example 3

[0091] Please refer to Figure 1 Specifically: the polarization assessment module includes a polarization response analysis unit and a polarization risk assessment unit;

[0092] The polarization response analysis unit is used to calculate based on the acquired polarization response data set to obtain the polarization response control index fpj, which is used to determine whether the insulation layer has polarization abnormalities due to humidity and aging. The specific formula is as follows:

[0093] ;

[0094] In the formula, ln represents the logarithmic function, dt represents the time calculus, and ts(t), il(t), and rh(t) represent the cable surface temperature, insulation surface leakage current, and ambient relative humidity at time t, respectively. This represents the temperature rise modulation factor, which suppresses the exponential amplification of the rate of change due to rapid temperature changes. The higher the temperature, the more severe the polarization hysteresis effect, and the smaller the amplification effect on the rate of change of leakage current. It represents the rate of increase of leakage current per unit time, which is the polarization dynamic response rate of the insulating material under electric field excitation. To reflect the nonlinear increasing nature of the influence of humidity, the polarization response behavior over time is accumulated by time integration to establish a trend function of polarization response behavior over time.

[0095] The polarization risk assessment unit is used to preset the insulation polarization risk threshold A by the user based on the limit requirements of insulation performance under the standard state of the power supply pile, and to assess the polarization response with the real-time acquired polarization response control index fpj. The specific assessment scheme is as follows:

[0096] When the polarization response control index fpj ≤ insulation polarization risk threshold A, it indicates that the insulation layer has not formed a hysteretic polarization effect and maintains the normal start-up mode;

[0097] When the polarization response control index fpj > insulation polarization risk threshold A, it indicates that there is polarization accumulation in the insulation layer, and at this time, disturbance distribution analysis is performed.

[0098] In this embodiment, the polarization assessment module is composed of a polarization response analysis unit and a polarization risk assessment unit. First, the polarization response analysis unit calculates the polarization response data set composed of cable surface temperature, insulation leakage current and ambient humidity to construct the polarization response control index fpj. This index mathematically incorporates the temperature rise modulation factor, leakage current growth rate and its time integral form to comprehensively characterize the polarization behavior trend of the insulation material under the action of heat, humidity and electrical coupling.

[0099] The dielectric polarization behavior of the insulating layer is one of the fundamental causes of voltage collapse under low-voltage environments. In humid environments, water molecules penetrate the microporous structure of the insulator, causing it to exhibit a "capacitor-like" effect, resulting in a lag in the establishment of the applied electric field. The leakage current *il* on the surface of the insulating layer is a direct manifestation of this effect; its increasing trend... This indicates an enhanced polarization rate. High temperatures accelerate charge migration in the dielectric, amplifying the effect of polarization hysteresis; therefore, an increase in polarization hysteresis is introduced. Adjustment; the higher the humidity, the easier it is for polarization potential energy to accumulate, using This reflects nonlinear amplification. All these parameters can be acquired in real time using existing sensors and are directly related to the physical processes of polarization behavior.

[0100] This formula calculates the cumulative polarization intensity behavior in the time domain in integral form, aiming to reflect that polarization hysteresis is a process behavior, rather than an instantaneous abrupt change. Three terms constitute a physical coupling chain: the rate of change of leakage current... As the core driver, the cable surface temperature (ts) constrains the charge migration rate, while the ambient relative humidity (rh) reflects the nonlinear enhancement of the microscopic polarization. A logarithmic function is employed to avoid linear saturation distortion of the ambient relative humidity (rh) under high humidity conditions; while... To prevent the polarization rate of change in the high-temperature range from being over-responded by the system, the formula structure not only ensures physical rationality but also possesses numerical stability, making it easy to deploy as a core state variable for real-time operation in the control system.

[0101] The polarization risk assessment unit compares the polarization response control index fpj with the user-defined insulation polarization risk threshold A in real time. When the polarization response control index fpj exceeds the insulation polarization risk threshold A, the system determines it to be a polarization accumulation state and triggers the disturbance energy analysis process. This implementation method achieves quantitative modeling and early identification of the insulation layer polarization hysteresis behavior, effectively avoiding misjudgment or delayed response problems caused by traditional solutions that do not consider multi-physics coupling behavior. This significantly improves the insulation safety, autonomous judgment capability, and intelligent control accuracy of low-voltage power supply piles in complex environments, possessing strong engineering application value and expansion potential.

[0102] Example 4

[0103] Please refer to Figure 1 Specifically: the disturbance energy analysis module is used to perform disturbance distribution analysis when the polarization response assessment indicates the presence of polarization accumulation;

[0104] Disturbance distribution analysis is used to calculate based on disturbance data sets to obtain the asymmetric disturbance energy index asy, which is used to analyze whether the power supply system has formed a disturbance concentration area under the dual disturbance of magnetic field and current signal. The specific formula is as follows;

[0105] ;

[0106] In the formula, It represents the magnetic flux density gradient along the cable direction, used to analyze the degree of magnetic field inhomogeneity in space and whether there are regions of energy disturbance accumulation in the power supply path. This represents the composite disturbance intensity function, used to construct a composite index of spatial and temporal disturbances in the power supply waveform, control the instability of the integrated spatial field and abrupt changes in the temporal domain, and output an index of the severity of the disturbance. This represents the background field adjustment mask function, used to dynamically suppress the intensity value of composite perturbations.

[0107] In this embodiment, the disturbance energy analysis module is automatically activated when the polarization response control index fpj exceeds the insulation polarization risk threshold A in the polarization response assessment. It constructs a disturbance distribution analysis mechanism by calling the magnetic induction intensity cg, vertical magnetic field intensity bz, and conductor local current density dm from the disturbance data set, and calculates the magnetic induction intensity gradient. The spatial magnetic field inhomogeneity is assessed, and the asymmetric perturbation energy index asy is dynamically obtained by combining the composite perturbation intensity function and the background field adjustment mask function.

[0108] The output current waveform of low-voltage power supply piles is highly susceptible to electromagnetic coupling disturbances under high humidity and high load conditions. The spatial characteristics of these disturbances manifest as a gradient in magnetic induction intensity. Non-uniformity reflects local impedance abrupt changes and insulation structure asymmetry in the power supply path. Its temporal characteristic is reflected in the increase of local current density dm in the conductor, representing signal distortion caused by load switching. The vertical magnetic field strength bz indicates whether it is under high background interference. The combination of these three factors is a complete expression of the spatiotemporal distribution of interference energy and is the fundamental driving force behind the asymmetric power supply mode.

[0109] This formula integrates the spatial distribution and temporal abrupt changes of the disturbance using the Euclidean norm to construct a disturbance energy amplitude index, and then uses a nonlinear function... Background suppression adjustment is performed to avoid misjudgment. When the overall magnetic field is low, the function tends to 1 and does not suppress disturbances; when the magnetic field is high, local disturbances will be judged as background disturbances, and the system will not enter the misadjustment mode. This approach ensures that the control response is triggered only when "the disturbance is highly concentrated and the background is stable," demonstrating strong adaptive suppression capabilities.

[0110] This method can effectively identify concentrated areas of electric field disturbances caused by inrush current concentration and electromagnetic inhomogeneity, enabling quantitative modeling and suppression control of abnormal disturbance sources. Through this module, the system can proactively identify and intervene in disturbance trends before abnormal voltage fluctuations occur, overcoming the limitation of traditional electrical systems that only respond to disturbances after they occur. This significantly improves the system's stability perception dimension, disturbance trend early warning capability, and accuracy of local response to interference sources in non-uniform power supply environments.

[0111] Example 5

[0112] Please refer to Figure 1 Specifically: the integrated control module includes a dynamic control analysis unit and an evaluation control unit;

[0113] The dynamic control analysis unit is used to summarize and calculate the comprehensive dynamic control index vcm based on the polarization response control index fpj and the asymmetric disturbance energy index asy. This index is used to couple the polarization anomaly with the disturbance trend synchronously to generate a precise dynamic voltage regulation control function, thereby realizing intelligent soft compensation adjustment of the startup process. The specific formula is as follows.

[0114] ;

[0115] In the formula, fpj(t) and asy(t) represent the polarization response control exponent and the asymmetric disturbance energy exponent at time t, respectively; T0 represents the user-defined adjustment period; and sin represents the sine function. dt represents the mathematical constant pi, taken to two decimal places, and dt represents the time calculus. This represents a sinusoidal window function, used to construct a time window adjustment function to prevent sudden changes in control output, enabling "gradual start-up, enhancement, and gradual shutdown" of regulation, matching the actual control response pattern. By integrating the output trend over all time points using the integral term, a decision quantity for adjusting the voltage is formed, and the evolution of historical states is analyzed to perform trend-based control response.

[0116] The assessment and control unit includes a disturbance assessment unit and a control instruction unit;

[0117] The disturbance assessment unit is used to collect all historical comprehensive dynamic control indices vcm, calculate the average value, set a disturbance asymmetry tolerance threshold B based on the average value, and then perform an electric field disturbance risk assessment with the real-time acquired comprehensive dynamic control index vcm. The specific assessment scheme is as follows:

[0118] When the comprehensive dynamic control index vcm ≤ the disturbance asymmetry tolerance threshold B, it means that the electric field disturbance is within a safe range. At this time, the gradual increase control command is executed for regulation.

[0119] When the comprehensive dynamic control index vcm > the disturbance asymmetry tolerance threshold B, it indicates that the electric field disturbance is unbalanced. At this time, the dual-mode control command of strong soft start and pulse regulation compensation is executed for regulation.

[0120] The control command unit is used to execute control commands after the electric field disturbance risk assessment, as follows;

[0121] When the gradual ascent control command is executed, the control behavior of the command includes:

[0122] The duty cycle adjustment module of the PWM controller adjusts the duty cycle rise gradient control value from the original setting to 130% of the original value, thereby extending the voltage rise time from 200ms to 260ms.

[0123] Power MOS current limiting module: By controlling the rise rate of the MOS gate voltage, the peak startup current is limited to 80% of the original set value;

[0124] RC filter control module: Adjusts the filter bandwidth to reduce the output voltage rise slope by 25% to prevent spike-induced insulation collapse;

[0125] When executing the dual-mode control command of strong soft start and pulse regulation compensation, the control behavior of the command includes:

[0126] PWM timer slope limiting module: extends the duty cycle growth period to the original setting of 180%, increasing the total voltage rise time to 360ms;

[0127] Output voltage redundancy control module: controls the redundancy fluctuation band of the target voltage by dynamically adjusting the reference source to ±7%;

[0128] Periodic perturbation modulation module: Through the Dither control strategy, the PWM period is generated with a micro-perturbation of ±0.2ms to avoid overlapping with the electromagnetic resonance frequency band;

[0129] High duty cycle limiting module: Forces the duty cycle to not exceed 85% of the rated value to prevent excessive instantaneous energy from being injected into weak insulation areas.

[0130] In this embodiment, the dynamic control analysis unit and disturbance evaluation unit under the integrated control module construct a comprehensive dynamic control index vcm calculation mechanism based on the synchronous summation of the polarization response control index fpj and the asymmetric disturbance energy index asy. This formula is the core output module of the entire control system, used to control the voltage compensation behavior of the low-voltage pile during startup under extreme conditions. It uses the polarization response control index fpj(t) and the asymmetric disturbance energy index asy(t) at time t of the polarization lag state as core factors. Only when both reach high values ​​simultaneously will the system generate a large dynamic compensation output. A sinusoidal window function is introduced. This is to simulate the "soft start slope" logic in the physical control curve, ensuring that the voltage compensation behavior has a control rhythm of natural rise and gradual fall, and avoiding overshoot caused by sudden large voltage regulation.

[0131] The formula structure embodies a typical "productive behavioral linkage function." The polarization response control index fpj(t) and the asymmetric disturbance energy index asy(t) at time t are two triggering factors with different physical dimensions. Under window function modulation, they are integrated and accumulated to form the final compensation signal, ensuring that the system makes judgments based on historical data behavior trends, improving anti-jitter capability, possessing good dynamic responsiveness and direction awareness, and exhibiting a "bell-shaped response" in the output. The peak control is in the middle stage, and the first and last stages approach 0, which conforms to the principle of electronic soft start. The denominator T0 limits the control period window and is used to normalize the overall adjustment speed and response amplitude. The overall structure is a composite model of behavioral collaborative linkage enhancement, time window regulation, and filtering and noise reduction stabilization mechanism, which can ensure that the compensation behavior has the triple attributes of intelligence, adaptability, and physical interpretability. The comprehensive dynamic regulation index vcm output by this function can be directly fed into the PWM module to adjust the voltage rise slope and current loading rate in the initial stage of power supply, which is one of the most critical regulation signals in the actual control system.

[0132] Furthermore, intelligent identification and adaptive response adjustment of electric field disturbance risks are achieved by dynamically setting the disturbance asymmetry tolerance threshold B. Specifically, based on the real-time relationship between the comprehensive dynamic control index vcm and the disturbance asymmetry tolerance threshold B, a dual-mode control strategy of "gradual rise control" or "strong soft start and pulse adjustment compensation" is precisely matched. Through multi-dimensional coordinated control of parameters such as PWM duty cycle rise gradient, MOS current limiting rate, filter bandwidth, periodic disturbance fine-tuning, and high duty cycle limitation, flexible correction of the voltage rise curve during startup and energy modulation of electromagnetic disturbance peaks are achieved. Compared to traditional single-threshold triggering or fixed-period rise methods, this solution possesses higher adaptability and risk suppression capabilities, significantly improving the insulation safety margin, voltage regulation accuracy, and overall system stability of the power supply pile under high humidity, polarization accumulation, and current disturbance environments, achieving the goal of intelligent, controllable, and refined voltage startup compensation control.

[0133] Example 6

[0134] Please refer to Figure 2 A method for intelligent optimization control of low-voltage power supply piles includes the following steps:

[0135] S1. Based on the installed sensor group, monitor the operation data of the power supply pile in real time, and preprocess the operation data to obtain polarization response data group and disturbance data group;

[0136] S2. Calculate the polarization response control index fpj based on the polarization response data set, and set the insulation polarization risk threshold A and the polarization response control index fpj to evaluate the polarization response.

[0137] S3. When the polarization response assessment indicates the presence of polarization accumulation, the asymmetric perturbation energy index asy is obtained by calculation based on the perturbation data set.

[0138] S4. Based on the polarization response control index fpj and the asymmetric disturbance energy index asy, the comprehensive dynamic control index vcm is obtained. The disturbance asymmetric tolerance threshold B and the comprehensive dynamic control index vcm are used to conduct an electric field disturbance risk assessment. Then, the control command is executed based on the assessment results.

[0139] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart optimization control system for low-voltage power supply piles, characterized in that: It includes a synchronous sensing module, a polarization assessment module, a disturbance energy analysis module, and a comprehensive control module; The synchronous sensing module is used to monitor the operation data of the power supply pile in real time based on the installed sensor group, and to preprocess the operation data to obtain polarization response data group and disturbance data group; The polarization assessment module is used to calculate based on the polarization response data set, obtain the polarization response control index fpj, and set the insulation polarization risk threshold A and the polarization response control index fpj to perform polarization response assessment. The perturbation energy analysis module is used to calculate and obtain the asymmetric perturbation energy index asy based on the perturbation data set when the polarization response assessment indicates the presence of polarization accumulation. The integrated control module is used to calculate the integrated dynamic control index vcm by summarizing the polarization response control index fpj and the asymmetric disturbance energy index asy. It also sets the disturbance asymmetric tolerance threshold B and the integrated dynamic control index vcm to conduct electric field disturbance risk assessment, and then executes control commands based on the assessment results.

2. The intelligent optimization control system for low-voltage power supply piles according to claim 1, characterized in that: The synchronous sensing module includes a data sensing unit and a data processing unit; The data sensing unit is used to monitor the operating data of the low-voltage power supply pile in real time based on the sensor group installed inside the low-voltage power supply pile and on the surface of the power supply cable insulation layer. The sampling period of the sensor group is controlled by a synchronous clock generated by the same RTC, and the monitored operating data is stamped with a μs-level timestamp. The sensor group includes an IR thermopile sensor, a flexible patch current sensor, a humidity sensor, a multi-point array Hall sensor, and a miniature current density sensor array. An IR thermopile sensor is spirally attached to the outer wall of the cable insulation layer to monitor the cable surface temperature ts in real time, which represents the thermal response state of the insulation material and correlates the polarization response rate. A flexible patch current sensor is embedded in the outer wrapping tape of the insulating layer to form a contact coupling circuit, which monitors the leakage current il on the surface of the insulating layer in real time, indicating the electric field leakage status of the insulating layer under humid conditions. The relative humidity (rh) of the environment is monitored in real time by a humidity sensor installed near the power supply pile, which directly affects the polarization degree and the moisture absorption rate of the medium. Based on the spatial distribution characteristics of the power supply channels inside the power supply pile, multi-point array Hall sensors are installed in sequence to monitor the magnetic induction intensity cg and the vertical magnetic field intensity bz in real time, which respectively represent the local magnetic disturbance during transient inrush and the peak value of the local density of the inrush during the initial stage of power supply anomaly. A miniature current density sensor array is sequentially installed around the copper core of the cable to monitor the local current density dm of the conductor in real time, which represents the actual current density flowing inside the conductor per unit cross-sectional area.

3. The intelligent optimization control system for low-voltage power supply piles according to claim 2, characterized in that: The data processing unit is used to preprocess the real-time acquired operating data to obtain polarization response data sets and disturbance data sets; The preprocessing includes dimensionless processing, noise reduction, missing value processing, and outlier processing. The dimensionless processing uses Z-Score standardization to perform standard transformation on the multiphysics data. The denoising uses multidimensional filtering technology to suppress noise in the running data, decompose and eliminate the noise influence in the data. Missing value processing uses the mean imputation method to fill in the missing values ​​in the dataset. Outlier processing uses the interquartile range method to detect and process outliers in the collected running data. The polarization response data set includes cable surface temperature ts, insulation surface leakage current il, and ambient relative humidity rh; The disturbance data set includes magnetic induction intensity cg, vertical magnetic field intensity bz, and conductor local current density dm.

4. The intelligent optimization control system for low-voltage power supply piles according to claim 3, characterized in that: The polarization assessment module includes a polarization response analysis unit and a polarization risk assessment unit; The polarization response analysis unit is used to calculate based on the acquired polarization response data set to obtain the polarization response control index fpj, which is used to determine whether the insulation layer has polarization abnormalities due to humidity and aging. The specific formula is as follows: ； In the formula, ln represents the logarithmic function, dt represents the time integral quantity, and ts(t), il(t), and rh(t) represent the cable surface temperature, insulation surface leakage current, and ambient relative humidity at time t, respectively.

5. The intelligent optimization control system for low-voltage power supply piles according to claim 4, characterized in that: The polarization risk assessment unit is used to preset the insulation polarization risk threshold A by the user based on the limit requirements of insulation performance under the standard state of the power supply pile, and to assess the polarization response with the real-time acquired polarization response control index fpj. The specific assessment scheme is as follows: When the polarization response control index fpj ≤ insulation polarization risk threshold A, it indicates that the insulation layer has not formed a hysteretic polarization effect and maintains the normal start-up mode; When the polarization response control index fpj > insulation polarization risk threshold A, it indicates that there is polarization accumulation in the insulation layer, and at this time, disturbance distribution analysis is performed.

6. The intelligent optimization control system for low-voltage power supply piles according to claim 5, characterized in that: The disturbance energy analysis module is used to perform disturbance distribution analysis when the polarization response is assessed as having polarization accumulation; Disturbance distribution analysis is used to calculate based on disturbance data sets to obtain the asymmetric disturbance energy index asy, which is used to analyze whether the power supply system has formed a disturbance concentration area under the dual disturbance of magnetic field and current signal. The specific formula is as follows; ； In the formula, This represents the gradient of magnetic induction intensity along the direction of the cable.

7. The intelligent optimization control system for low-voltage power supply piles according to claim 6, characterized in that: The integrated control module includes a dynamic control analysis unit and an evaluation control unit; The dynamic control analysis unit is used to summarize and calculate the comprehensive dynamic control index vcm based on the polarization response control index fpj and the asymmetric disturbance energy index asy. This index is used to couple the polarization anomaly with the disturbance trend synchronously to generate a precise dynamic voltage regulation control function, thereby realizing intelligent soft compensation adjustment of the startup process. The specific formula is as follows. ； In the formula, fpj(t) and asy(t) represent the polarization response control exponent and the asymmetric disturbance energy exponent at time t, respectively; T0 represents the user-defined adjustment period; and sin represents the sine function. dt represents pi, with a value to two decimal places, and dt represents the time calculus.

8. The intelligent optimization control system for low-voltage power supply piles according to claim 7, characterized in that: The assessment and control unit includes a disturbance assessment unit and a control instruction unit; The disturbance assessment unit is used to collect all historical comprehensive dynamic control indices vcm, calculate the average value, set a disturbance asymmetry tolerance threshold B based on the average value, and then perform an electric field disturbance risk assessment with the real-time acquired comprehensive dynamic control index vcm. The specific assessment scheme is as follows: When the comprehensive dynamic control index vcm ≤ the disturbance asymmetry tolerance threshold B, it means that the electric field disturbance is within a safe range. At this time, the gradual increase control command is executed for regulation. When the comprehensive dynamic control index vcm > the disturbance asymmetry tolerance threshold B, it indicates that the electric field disturbance is unbalanced. At this time, the dual-mode control command of strong soft start and pulse regulation compensation is executed for regulation.

9. The intelligent optimization control system for low-voltage power supply piles according to claim 8, characterized in that: The control command unit is used to execute control commands after the electric field disturbance risk assessment, as follows; When the gradual ascent control command is executed, the control behavior of the command includes: The duty cycle adjustment module of the PWM controller adjusts the duty cycle rise gradient control value from the original setting to 130% of the original value, thereby extending the voltage rise time from 200ms to 260ms. Power MOS current limiting module: By controlling the rise rate of the MOS gate voltage, the peak startup current is limited to 80% of the original set value; RC filter control module: Adjusts the filter bandwidth to reduce the output voltage rise slope by 25%; When executing the dual-mode control command of strong soft start and pulse regulation compensation, the control behavior of the command includes: PWM timer slope limiting module: extends the duty cycle growth period to the original setting of 180%, increasing the total voltage rise time to 360ms; Output voltage redundancy control module: controls the redundancy fluctuation band of the target voltage by dynamically adjusting the reference source to ±7%; Periodic perturbation modulation module: Through the Dither control strategy, a micro-perturbation of ±0.2ms is generated in the PWM period; High duty cycle limiting module: forces the duty cycle to not exceed 85% of the rated value.

10. A method for intelligent optimization control of low-voltage power supply piles, applied to the intelligent optimization control system for low-voltage power supply piles as described in any one of claims 1-9, characterized in that: Includes the following steps: S1. Based on the installed sensor group, monitor the operation data of the power supply pile in real time, and preprocess the operation data to obtain polarization response data group and disturbance data group; S2. Calculate the polarization response control index fpj based on the polarization response data set, and set the insulation polarization risk threshold A and the polarization response control index fpj to evaluate the polarization response. S3. When the polarization response assessment indicates the presence of polarization accumulation, the asymmetric perturbation energy index asy is obtained by calculation based on the perturbation data set. S4. Based on the polarization response control index fpj and the asymmetric disturbance energy index asy, the comprehensive dynamic control index vcm is obtained. The disturbance asymmetric tolerance threshold B and the comprehensive dynamic control index vcm are used to conduct an electric field disturbance risk assessment. Then, the control command is executed based on the assessment results.

Images