Automobile charging pile dynamic power distribution system based on multiple power modules

Abstract

The invention discloses an automobile charging pile dynamic power distribution system based on multiple power modules, and relates to the technical field of automobile charging piles, a data monitoring module monitors the harmonic component of each power module and the charging demand of an automobile in real time, an analysis module calculates the harmonic abnormal index according to the harmonic component data, and feeds back the harmonic abnormal index to a regulation and control module; and the regulation and control module analyzes the charging demand and the harmonic abnormal index by applying fuzzy logic and dynamically adjusts the output frequency and phase of each power module, so that the harmonic interference risk is effectively reduced, the charging efficiency, stability and safety of the system are improved, the charging demand and harmonic suppression can be balanced, and intelligent power distribution can be realized. And stable and reliable operation of a power grid and charging equipment is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile charging piles, and in particular to a dynamic power distribution system for an automobile charging pile based on a multi-power module. Background Art

[0002] Dynamic power allocation for car charging piles based on multiple power modules refers to a charging pile design and control method that realizes efficient and flexible charging of electric vehicles through the coordinated work of multiple power modules. This system contains multiple independent power modules, and the output power of each module can be adjusted according to the charging demand and actual situation, so as to optimize the charging efficiency, reduce energy loss, and improve the stability and flexibility of the system. Specifically, dynamic power allocation can automatically adjust the output of each power module according to the battery power, charging stage and load of the external power grid of different cars. However, during the dynamic power allocation process, each power module may generate harmonic components when the charging load changes rapidly. Harmonics will interfere with the voltage and current waveforms of the power grid. If the power grid in the area where the charging pile is located is sensitive to harmonics, it may cause resonance. Resonance will cause the current and voltage to superimpose on each other in the system, causing the local line voltage to rise sharply, thereby damaging the power equipment and even causing a fire. At the same time, the dynamic power allocation system relies on real-time monitoring of current and voltage to dynamically adjust the output according to the power grid status and the battery requirements of each vehicle. If the voltage and current fluctuations caused by harmonic resonance cause abnormalities in the system monitoring data, it may cause control logic disorder. The current signal error caused by harmonics may cause the system to misjudge the vehicle's charging needs. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic power distribution system for a car charging pile based on multiple power modules to solve the shortcomings of the background technology.

[0004] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a dynamic power distribution system for a car charging pile based on a multi-power module, comprising a data monitoring module, an analysis module and a control module; The data monitoring module is electrically connected to the power module and is used to monitor the harmonic component data between different power modules and the power grid during the charging load change process in several areas, as well as the real-time charging demand data of the vehicle; The analysis module is connected to the data monitoring module and the power module, and is used to receive the harmonic component data fed back by the data monitoring module, and determine the weight assignment of the harmonic component data between each power module and the power grid, and obtain the harmonic anomaly index between the power grid and the power module after weighted summation calculation of the weight assignment of the harmonic component data between each power module and the power grid; The control module is connected to the power grid and the power module, and is used to receive the real-time charging demand data sent by the data module and the harmonic anomaly index sent by the analysis module, and dynamically adjust the output frequency and phase of each power module after analyzing them through fuzzy logic.

[0005] Preferably, the data monitoring module is used to monitor the harmonic component data between different power modules and the power grid during the charging load changes in several areas, wherein the several area divisions specifically include: the charging station is divided into areas according to the physical location, and the charging piles in each area share a data monitoring module; the charging piles with the same and similar power inputs are divided into one area; the areas are divided according to different charging load requirements, and the high-power areas and ordinary power areas are divided. The charging piles in the high-power area are suitable for fast charging services, while the ordinary power area is used for slow charging needs; the regional division is dynamically adjusted according to the actual charging load situation, that is, the areas are re-divided according to the usage of the charging piles during fixed time periods.

[0006] Preferably, in the analysis module, a power grid harmonic impedance index is generated after analyzing the degree of obstruction of the power grid to harmonic current at different frequencies. The method for obtaining the power grid harmonic impedance index is: The instantaneous signal data of current and voltage are collected from the power grid. The sampling frequency is selected to be twice higher than the target harmonic frequency. Fast Fourier transform is applied to the collected current I(t) and voltage V(t) time domain signals to convert them into the frequency domain to obtain the amplitude and phase of each harmonic frequency. The calculation formula is: ; Where I(f) and V(f) are the complex forms of current and voltage in the frequency domain, respectively. For each harmonic frequency f, the harmonic impedance Z(f) is calculated using the following formula: The expression for harmonic impedance is: ; Where: Z(f) is the harmonic impedance at frequency f, V(f) and I(f) are the amplitudes of voltage and current at frequency f, respectively. For each harmonic frequency f, the corresponding Z(f) value is calculated to represent the impedance characteristics of the power grid at that frequency; Set the harmonic frequency range [fmin,fmax] and the resolution increment Δf, and the frequency resolution of the fast Fourier transform after adaptive adjustment The expression is: ; Where N is the increased number of sampling points. According to the impedance Z(f) at each harmonic frequency, the overall harmonic impedance index is calculated by weighted average, and the expression is: ; F is the set of all harmonic frequencies, and ZP is the power grid harmonic impedance index.

[0007] Preferably, after analyzing the fluctuation of the charging load change rate of each power module within a unit time, a load fluctuation rate abnormality index is generated, and the method for obtaining the load fluctuation rate abnormality index is: Obtain the load fluctuation rate data of each power module per unit time. The load fluctuation rate is defined as the rate of change of current. The data set is recorded as , represents the load fluctuation rate at time t; determine the smoothing coefficient α, the value range is: 0<α≤1; calculate the exponentially weighted moving average , the calculation formula is: ;in: is the exponentially weighted moving average of the previous moment. At the first time point of calculation, Set as initial load fluctuation rate , at each moment t, by comparing the actual load fluctuation rate and The difference between them is used to calculate the load fluctuation abnormality index, and the expression is: ; Where SF is the load fluctuation abnormality index.

[0008] Preferably, the power grid harmonic impedance index and the load fluctuation rate anomaly index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as inputs of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the weighted assignment labels of the harmonic component data between each power module and the power grid as the prediction target, and takes minimizing the sum of the prediction errors of the weighted assignment labels of the harmonic component data between all power modules and the power grid as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence and the model training is stopped. The weight assignment of the harmonic component data between each power module and the power grid is determined according to the model output results, wherein the machine learning model is a polynomial regression model, and the harmonic anomaly index between the power grid and the power module is obtained by weighted summing the weight assignments of the harmonic component data between each power module and the power grid.

[0009] Preferably, the control module is used to receive the real-time charging demand data sent by the data module and the harmonic anomaly index sent by the analysis module, and use the real-time charging demand data and the harmonic anomaly index as input items of the fuzzy logic, and use the output frequency and phase of the power module as output items of the fuzzy logic; The real-time charging demand data, harmonic anomaly index, and the output frequency and phase of the power module are fuzzy processed to construct fuzzy rules. Fuzzy reasoning is used to comprehensively analyze the inputs, and the adjustment strategy of the power module is calculated according to the fuzzy rules.

[0010] Preferably, the charging demand and the harmonic anomaly index are fuzzified to obtain their corresponding membership degrees; according to the set fuzzy rule table, the rules that meet the conditions are matched; the fuzzy reasoning engine is used to calculate the rules that meet the conditions to determine the fuzzy values ​​of the output frequency and phase; the output result of the fuzzy reasoning is a fuzzy set, which is converted into a specific numerical output, i.e., the actual frequency and phase value of the power module, through defuzzification processing, and the control module adjusts the output frequency and phase of each power module in real time according to the defuzzified output value.

[0011] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes efficient charging of electric vehicles while effectively suppressing harmonic interference caused by load fluctuations through a multi-module collaborative dynamic power distribution system. The data monitoring module monitors the charging load and harmonic components in each area in real time, providing refined data support; the analysis module dynamically calculates the harmonic anomaly index and load fluctuation rate anomaly index through fast Fourier transform (FFT) and machine learning models to quantify the impedance characteristics of the power grid to harmonics and the risk of load fluctuations, ensuring the accuracy of power regulation. This method can adapt to changes in different regions and different charging needs, optimize the system's harmonic detection and control performance, and greatly improve the stability and safety of the charging system.

[0012] 2. The control module in the present invention is based on fuzzy logic algorithm, which can flexibly handle complex charging requirements and harmonic anomaly index, and adjust the output frequency and phase of the power module in real time. This fuzzy logic reasoning process improves the response speed of the system, making the charging pile more intelligent and efficient when dealing with rapidly changing loads and harmonic interference. Through fuzzification processing and defuzzification conversion, the control module can automatically optimize the output under different charging stages and harmonic conditions, effectively reducing the risk of grid resonance and improving the overall power quality and energy efficiency of the charging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] For examples, see Figure 1 As shown, the dynamic power distribution system of a vehicle charging pile based on multiple power modules described in this embodiment includes a data monitoring module, an analysis module and a control module; The data monitoring module is electrically connected to the power module and is used to monitor the harmonic component data between different power modules and the power grid during the charging load change process in several areas, as well as the real-time charging demand data of the vehicle; The analysis module is connected to the data monitoring module and the power module, and is used to receive the harmonic component data fed back by the data monitoring module, and determine the weight assignment of the harmonic component data between each power module and the power grid, and obtain the harmonic anomaly index between the power grid and the power module after weighted summation calculation of the weight assignment of the harmonic component data between each power module and the power grid; The control module is connected to the power grid and the power module, and is used to receive the real-time charging demand data sent by the data module and the harmonic anomaly index sent by the analysis module, and dynamically adjust the output frequency and phase of each power module after analyzing them through fuzzy logic.

[0017] In order to facilitate the monitoring of harmonic interference and load distribution, the area division of the charging station should follow the actual requirements of physical layout and electrical connection. The area can usually be divided based on the following methods: Physical location division: Charging stations are divided into areas according to physical locations, such as each group of charging piles (for example, a group of 10) is an area. The charging piles in each area share a data monitoring module to collect the current, voltage, and harmonic data of the area in real time. This method is suitable for large charging stations and can effectively monitor changes in distributed loads.

[0018] Electrical connection division: Charging piles with the same or similar power input are divided into one area. For example, if some charging piles are connected to the same transformer or power branch, these charging piles can be considered as the same area. This division helps to accurately monitor the harmonic resonance on the branch and is suitable for complex electrical system layouts.

[0019] Load demand division: Divide the area according to different charging load demands, such as high power area and normal power area. Charging piles in the high power area are suitable for fast charging services, while normal power areas are used for slow charging needs. Due to the drastic load changes in the high power area, the possibility of harmonic generation is higher, and more sophisticated harmonic monitoring is required.

[0020] Dynamically adjustable zone division: Some charging stations can dynamically adjust zone division according to the actual charging load, that is, re-dividing the zone according to the usage of charging piles during a specific period of time. This dynamic zoning method can improve the utilization rate of the monitoring module, especially for places with multiple frequency load changes.

[0021] The division of each area must ultimately be combined with site design, electrical network and load characteristics to ensure independent monitoring and management of data in each area, so that the power module can adjust the output in a targeted manner to avoid the superposition and resonance of harmonics.

[0022] In order to achieve dynamic power allocation, the charging pile system needs to obtain the real-time charging demand data of each vehicle in a timely manner, including battery power, voltage demand, charging power demand, etc. This data can be obtained in the following ways: Communication protocol between vehicle and charging pile: Modern electric vehicles and charging piles usually use standard communication protocols (such as CHAdeMO, CCS (combined charging system), GB / T protocol) to exchange data. These protocols allow the charging pile to obtain the vehicle's battery information, including the current state of charge (SOC, State of Charge), battery temperature, charging power requirements and other data. In this way, the charging pile can obtain and update the vehicle's charging requirements in real time in order to adjust the power output.

[0023] Vehicle Battery Management System (BMS) data interaction: BMS (Battery Management System) directly monitors and manages the status of the battery, such as voltage, current, temperature, etc. The charging pile can communicate with the BMS to obtain detailed battery demand data in real time. For example, the BMS will automatically adjust the required current and voltage according to the actual charging status of the battery. This data will be transmitted to the charging pile control unit to dynamically adjust the output of the power module to achieve optimal power distribution.

[0024] Load monitoring device embedded in charging pile: Charging pile can be embedded with a dedicated load monitoring module to measure the actual load changes of connected vehicles in real time. This device can indirectly judge the current charging needs of the vehicle by detecting the real-time fluctuations of charging current and voltage. For example, when the vehicle enters the constant current charging stage, the load monitoring device will detect the change in current and adjust the output accordingly to ensure that the charging needs of the vehicle are met while optimizing the harmonic distribution.

[0025] Wireless communication module and cloud platform data sharing: In some highly intelligent charging stations, the charging requirements of vehicles can be uploaded to the cloud platform of the charging pile control system through wireless communication (such as 4G / 5G network). The cloud platform will analyze the historical charging data of each vehicle and provide charging demand predictions based on the data model during the charging process. Through the wireless communication module, the charging pile receives updated data from the cloud platform in real time to dynamically optimize power distribution.

[0026] In this application, regional division and acquisition of vehicle charging demand data are the core of dynamic power allocation. Regional division is effectively managed through physical layout, electrical connection or load demand to centrally monitor the harmonic conditions in each area. The acquisition of charging demand data relies on standard communication protocols, BMS data interaction, load monitoring and cloud platform data sharing to ensure that the charging pile responds accurately and quickly and optimizes the output when the load changes. The combination of these means enables the charging pile to achieve dynamic power allocation smoothly and effectively in the fluctuation of charging demand.

[0027] In the dynamic power distribution system, the analysis module needs to use the harmonic component data to calculate and evaluate the harmonic anomaly in each area, so as to achieve dynamic adjustment of the charging pile system. To this end, the analysis module calculates the harmonic anomaly index through weight assignment and weighted summation, which can reflect the degree of harmonic interference between the power grid and the power module in each area.

[0028] The analysis module receives real-time harmonic component data from different areas from the data monitoring module. The data monitoring module records the harmonic frequency, harmonic amplitude, phase and other information of each area in real time according to the working status of each power module, current and voltage fluctuations. This data can effectively reflect the harmonic components generated by each power module during the charging load change process.

[0029] The grid harmonic impedance index is generated by analyzing the degree of resistance of the grid to harmonic current at different frequencies. The method for obtaining the grid harmonic impedance index is as follows: Collect instantaneous signal data of current and voltage from the power grid. These signals can be measured in real time by sensors within the power grid, and the data sampling frequency should be high enough to cover the harmonic frequency range that needs to be analyzed. The sampling frequency is usually selected to be twice higher than the target harmonic frequency (satisfying the Nyquist sampling theorem) to ensure signal integrity and accuracy. For example, if the highest frequency of the target harmonic is 1000 Hz, the sampling frequency should be at least 2000 Hz.

[0030] Fast Fourier transform is applied to the collected current I(t) and voltage V(t) time domain signals to convert them into the frequency domain to obtain the amplitude and phase of each harmonic frequency. The calculation formula is: ; Where I(f) and V(f) are the complex forms of current and voltage in the frequency domain, respectively, and contain the amplitude and phase information of each frequency. For each harmonic frequency f (for example, integer multiples of the fundamental frequency, such as the 2nd harmonic, 3rd harmonic, etc.), the harmonic impedance Z(f) is calculated using the following formula: The expression of harmonic impedance is: ; Where: Z(f) is the harmonic impedance at frequency f, in ohms (Ω), V(f) and I(f) are the amplitudes (or complex forms, including phase) of the voltage and current at frequency f, respectively. For each harmonic frequency f, calculate the corresponding Z(f) value to represent the impedance characteristics of the power grid at that frequency. Adaptive harmonic analysis is used to refine the resolution of a specific frequency range to improve the accurate detection of key harmonic components. The frequency resolution is usually adjusted by increasing the number of sampling points to improve the frequency resolution of the FFT. Alternatively, a windowing method (such as Hanning window, Blackman window, etc.) is used in the key frequency range (such as the 3rd and 5th harmonics) to reduce the sidelobe effect, thereby improving the accuracy of harmonic detection. Set the harmonic frequency range [fmin, fmax] and the resolution increment Δf, and the frequency resolution of the fast Fourier transform after adaptive adjustment. The expression is: ; Where N is the increased number of sampling points. According to the impedance Z(f) at each harmonic frequency, the overall harmonic impedance index is calculated by weighted average, and the expression is: ; F is the set of all harmonic frequencies (such as the fundamental frequency and its multiple frequencies), and ZP is the power grid harmonic impedance index.

[0031] The larger the grid harmonic impedance index is, the higher the degree of resistance of the grid to harmonic currents at each harmonic frequency. This generally indicates that the grid is more sensitive to these harmonics, especially at specific frequencies where harmonic resonance may be more likely to occur. A high harmonic impedance index means that the power modules in the grid are more susceptible to high-frequency harmonic interference, which can cause voltage and current fluctuations and affect power quality. For charging pile systems, this situation can cause abnormally severe harmonics, such as unstable current waveforms, equipment overheating, frequent operation of protection devices, and even module damage or charging pile system shutdown. To avoid the risks caused by a high harmonic impedance index, the grid may need to take effective harmonic filtering measures or adjust load distribution to reduce harmonic sensitivity.

[0032] On the contrary, the smaller the grid harmonic impedance index is, the lower the degree of obstruction of the grid to harmonic current at each harmonic frequency, indicating that the grid has a higher tolerance for harmonics. A lower harmonic impedance index indicates that the harmonic anomaly between the power module and the grid is less severe, and the grid can better suppress the impact of harmonics and maintain relatively stable voltage and current waveforms. In this case, the power module of the charging pile system can operate more stably, reducing the fluctuations and interference caused by harmonics, and improving the power quality and safety of the equipment. However, a low impedance index does not mean that there is no harmonic impact at all, and continuous monitoring is still required to ensure the stability of grid operation, especially when the load changes greatly.

[0033] After analyzing the fluctuation of the charging load change rate of each power module within a unit time, a load fluctuation rate abnormality index is generated. The method for obtaining the load fluctuation rate abnormality index is as follows: Obtain the load fluctuation rate data of each power module per unit time. The load fluctuation rate is defined as the rate of change of current, such as the current change per second (ΔI). The data set can be recorded as , represents the load volatility at time t; the exponentially weighted moving average requires a smoothing coefficient α, which controls the weight distribution of recent data and historical data. When selecting the α value, it is necessary to balance the response speed and smoothness. The value range is: 0<α≤1; the closer the value is to 1, the higher the weight of the recent data, which means a faster response to changes, but the fluctuation may also be larger; the smaller the value, the smoother and slower the response speed. For example, the commonly used α value range is between 0.1 and 0.3. Calculating the exponentially weighted moving average , the calculation formula is: ;in: is the exponentially weighted moving average of the previous moment. At the first time point of calculation, Set as initial load fluctuation rate , at each moment t, by comparing the actual load fluctuation rate and The difference between them is used to calculate the load fluctuation abnormality index, and the expression is: ; Where SF is the load fluctuation abnormality index.

[0034] When the load fluctuation rate anomaly index is larger, it means that the load fluctuation rate of the current power module has obviously deviated from its smoothed value, indicating that the power module has experienced a drastic load change in a short period of time. This anomaly is usually associated with a higher harmonic content, because rapid load changes will lead to an increase in nonlinear components, resulting in stronger high-frequency harmonic interference. The increase in harmonic anomalies will not only affect the stability of the power module, but also cause large deviations in the voltage and current waveforms in the power grid, which may cause problems such as harmonic resonance or equipment overheating. Therefore, a higher load fluctuation rate anomaly index reflects the intensification of the degree of harmonic anomalies between the power grid and the power module, indicating that the system has a potential risk of harmonic interference.

[0035] On the contrary, when the load fluctuation rate anomaly index is smaller, it means that the load fluctuation rate of the power module is relatively stable, and the difference from its smoothed value is small. This stable load state usually corresponds to a lower harmonic content, because the changes in current and power tend to be slow, and fewer high-frequency harmonics are generated in the system. The low load fluctuation rate anomaly index reflects the low degree of harmonic anomaly between the power grid and the power module, which means that the power grid and equipment are operating under normal load conditions, the voltage and current waveforms are relatively smooth and stable, and the harmonic interference is small, which is conducive to the safe and stable operation of the system.

[0036] The power grid harmonic impedance index and the load fluctuation rate anomaly index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model predicts the weighted labels of the harmonic component data between each power module and the power grid with each group of comprehensive feature vectors as the prediction target, and minimizes the sum of the prediction errors of the weighted labels of the harmonic component data between all power modules and the power grid as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The weighted assignment of the harmonic component data between each power module and the power grid is determined according to the model output results, wherein the machine learning model is a polynomial regression model, and the harmonic anomaly index between the power grid and the power module is obtained by weighted summation calculation of the weighted assignments of the harmonic component data between each power module and the power grid.

[0037] The method for obtaining the weight assignment of the harmonic component data between each power module and the power grid is as follows: from the comprehensive feature vector training data of the trained machine learning model, the corresponding function expression is obtained: ; In the formula, is the output function of the model, ZP is the power grid harmonic impedance index, SF is the load fluctuation rate abnormality index, Assign weights to the harmonic component data between each power module and the power grid.

[0038] The control module uses fuzzy logic algorithms to analyze real-time charging demand data and harmonic anomaly index, thereby dynamically adjusting the output frequency and phase of each power module to reduce harmonic interference and meet charging needs. Fuzzy logic algorithms are particularly effective in dealing with complex, nonlinear systems, and can be flexibly adjusted through fuzzy rules.

[0039] The charging demand data received from the data module includes the current load (such as current or power demand) and battery status (such as remaining power, etc.), and the control module fuzzifies it, for example, dividing the charging demand into fuzzy sets such as "low", "medium", and "high".

[0040] The harmonic anomaly index received from the analysis module reflects the degree of harmonic interference. The control module can divide the anomaly index into fuzzy sets such as "mild", "medium", and "severe" to characterize the impact of harmonics on the system.

[0041] The control module needs to establish a set of fuzzy rules to determine how to adjust the output frequency and phase of the power module under different charging demands and harmonic anomaly levels. Typical examples of fuzzy rules include: if the charging demand is "high" and the harmonic anomaly index is "mild", the power module "high frequency and high phase output"; if the charging demand is "medium" and the harmonic anomaly index is "medium", the power module "medium frequency and low phase output"; if the charging demand is "low" and the harmonic anomaly index is "serious", the power module "low frequency and zero phase output". These rules can be set through expert knowledge or historical data, and gradually adjusted and optimized according to the actual operation of the system.

[0042] Use fuzzy reasoning (such as Mamdani fuzzy reasoning or Sugeno fuzzy reasoning) to comprehensively analyze the inputs and calculate the adjustment strategy of the power module based on fuzzy rules. The reasoning process involves: Fuzzify the charging demand and harmonic anomaly index to obtain their corresponding membership degrees; According to the set fuzzy rule table, match the rules that meet the conditions; These qualifying rules are evaluated using a fuzzy inference engine to determine the fuzzy values ​​of the output frequency and phase.

[0043] The output result of fuzzy reasoning is a fuzzy set, so it needs to be converted into a specific numerical output (i.e. the actual frequency and phase value of the power module) through defuzzification. Commonly used defuzzification methods include the centroid method, the maximum membership method, etc.

[0044] According to the defuzzified output value, the control module adjusts the output frequency and phase of each power module in real time: Through frequency regulation, the harmonic components of a specific frequency can be reduced to prevent harmonic resonance in the system. Frequency adjustment is performed according to charging demand to balance power demand and harmonic control. Through phase regulation, the phase difference between the outputs of each power module can be reduced, thereby reducing the superposition effect of harmonic components on the power grid and improving the overall power stability.

[0045] In this application, the control module uses fuzzy logic algorithm to comprehensively analyze the real-time charging demand and harmonic anomaly index, thereby dynamically and intelligently adjusting the output frequency and phase of each power module. This adaptive control method can effectively reduce harmonic interference and meet different charging needs, thereby improving the system's operating efficiency and the stability of the power grid.

[0046] In this embodiment, the data monitoring module, the analysis module and the control module work together to achieve intelligent management of charging loads and harmonic interference in multiple areas. The data monitoring module is responsible for real-time monitoring of the harmonic component data between different power modules and the power grid, and obtaining the charging demand data of the vehicle. The analysis module assigns weights and performs weighted summation calculations on the harmonic data between each power module and the power grid based on the harmonic component data fed back by the data monitoring module, thereby deriving a harmonic anomaly index and evaluating the degree of harmonic anomaly between the power grid and the power module. The control module receives real-time charging demand data and harmonic anomaly index, and after analyzing this information through fuzzy logic, dynamically adjusts the output frequency and phase of the power module to reduce harmonic interference and meet the charging needs of different areas, thereby achieving stable operation and efficient regulation of the system.

[0047] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0048] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0049] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A dynamic power distribution system for a car charging pile based on multiple power modules, characterized in that: Including data monitoring module, analysis module and control module; The data monitoring module is electrically connected to the power module and is used to monitor the harmonic component data between different power modules and the power grid during the charging load change process in several areas, as well as the real-time charging demand data of the vehicle; The analysis module is connected to the data monitoring module and the power module, and is used to receive the harmonic component data fed back by the data monitoring module, and determine the weight assignment of the harmonic component data between each power module and the power grid, and obtain the harmonic anomaly index between the power grid and the power module after weighted summation calculation of the weight assignment of the harmonic component data between each power module and the power grid; The control module is connected to the power grid and the power module to receive the real-time charging demand data sent by the data module and the harmonic anomaly index sent by the analysis module, and dynamically adjust the output frequency and phase of each power module after analyzing them through fuzzy logic.

2. According to claim 1, a dynamic power distribution system for a car charging pile based on multiple power modules is characterized in that: The data monitoring module is used to monitor the harmonic component data between different power modules and the power grid during the change of charging load in several areas. Among them, the division of several areas specifically includes: the charging station is divided into areas according to the physical location, and the charging piles in each area share a data monitoring module; the charging piles with the same and similar power inputs are divided into one area; the areas are divided according to different charging load requirements, and divided into high-power areas and ordinary power areas. The charging piles in the high-power area are suitable for fast charging services, while the ordinary power area is used for slow charging needs; the regional division is dynamically adjusted according to the actual charging load situation, that is, the areas are re-divided according to the usage of the charging piles during fixed time periods.

3. According to claim 1, a dynamic power distribution system for a car charging pile based on multiple power modules is characterized in that: In the analysis module, the power grid harmonic impedance index is generated after analyzing the degree of resistance of the power grid to harmonic current at different frequencies. The method for obtaining the power grid harmonic impedance index is as follows: The instantaneous signal data of current and voltage are collected from the power grid. The sampling frequency is selected to be twice higher than the target harmonic frequency. Fast Fourier transform is applied to the collected current I(t) and voltage V(t) time domain signals to convert them into the frequency domain to obtain the amplitude and phase of each harmonic frequency. The calculation formula is: ; Where I(f) and V(f) are the complex forms of current and voltage in the frequency domain, respectively. For each harmonic frequency f, the harmonic impedance Z(f) is calculated using the following formula: The expression for harmonic impedance is: ; Where: Z(f) is the harmonic impedance at frequency f, V(f) and I(f) are the amplitudes of voltage and current at frequency f, respectively. For each harmonic frequency f, the corresponding Z(f) value is calculated to represent the impedance characteristics of the power grid at that frequency; Set the harmonic frequency range [fmin,fmax] and the resolution increment Δf, and the frequency resolution of the fast Fourier transform after adaptive adjustment The expression is: ; Where N is the increased number of sampling points. According to the impedance Z(f) at each harmonic frequency, the overall harmonic impedance index is calculated by weighted average, and the expression is: ; F is the set of all harmonic frequencies, and ZP is the power grid harmonic impedance index.

4. According to claim 3, a dynamic power distribution system for a car charging pile based on multiple power modules is characterized in that: After analyzing the fluctuation of the charging load change rate of each power module within a unit time, a load fluctuation rate abnormality index is generated. The method for obtaining the load fluctuation rate abnormality index is as follows: Obtain the load fluctuation rate data of each power module per unit time. The load fluctuation rate is defined as the rate of change of current. The data set is recorded as , represents the load fluctuation rate at time t; determine the smoothing coefficient α, the value range is: 0<α≤1; calculate the exponentially weighted moving average , the calculation formula is: ;in: is the exponentially weighted moving average of the previous moment. At the first time point of calculation, Set as initial load fluctuation rate , at each moment t, by comparing the actual load fluctuation rate and The difference between them is used to calculate the load fluctuation abnormality index, and the expression is: ; Where SF is the load fluctuation abnormality index.

5. According to claim 4, a dynamic power distribution system for a car charging pile based on multiple power modules is characterized in that: The power grid harmonic impedance index and the load fluctuation rate anomaly index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model predicts the weighted labels of the harmonic component data between each power module and the power grid with each group of comprehensive feature vectors as the prediction target, and minimizes the sum of the prediction errors of the weighted labels of the harmonic component data between all power modules and the power grid as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The weighted assignment of the harmonic component data between each power module and the power grid is determined according to the model output results, wherein the machine learning model is a polynomial regression model, and the harmonic anomaly index between the power grid and the power module is obtained by weighted summation calculation of the weighted assignments of the harmonic component data between each power module and the power grid.

6. According to claim 1, a dynamic power distribution system for a car charging pile based on multiple power modules is characterized in that: In the control module, it is used to receive the real-time charging demand data sent by the data module and the harmonic anomaly index sent by the analysis module, and use the real-time charging demand data and the harmonic anomaly index as input items of fuzzy logic, and use the output frequency and phase of the power module as output items of fuzzy logic; The real-time charging demand data, harmonic anomaly index, and the output frequency and phase of the power module are fuzzy processed to construct fuzzy rules. Fuzzy reasoning is used to comprehensively analyze the inputs, and the adjustment strategy of the power module is calculated according to the fuzzy rules.

7. The dynamic power distribution system for automobile charging piles based on multiple power modules according to claim 6 is characterized in that: The charging demand and harmonic anomaly index are fuzzified to obtain their corresponding membership degrees; according to the set fuzzy rule table, the rules that meet the conditions are matched; the fuzzy reasoning engine is used to calculate the rules that meet the conditions to determine the fuzzy values ​​of the output frequency and phase; the output result of the fuzzy reasoning is a fuzzy set, which is converted into a specific numerical output, i.e. the actual frequency and phase value of the power module, through defuzzification processing. According to the defuzzified output value, the control module adjusts the output frequency and phase of each power module in real time.

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