Charging pile fault online detection method and system

Abstract

The invention discloses an online fault detection method and system for a charging pile, and relates to the technical field of intelligent monitoring of the charging pile, and the system monitors the filtering performance data of the charging pile in real time through an installed monitoring device, and transmits the data to a central control system for preprocessing through the Internet. The method comprises the following steps: calculating a ripple dynamic evolution index ahf, carrying out filter health assessment, calculating a higher harmonic comprehensive energy index xnl and a high-frequency energy distribution response index xpb when the filter health assessment shows that an aging sign exists, then comprehensively calculating a comprehensive health evolution index FHI to carry out comprehensive filter performance assessment, and when the filter has a failure risk, carrying out comprehensive filter performance assessment. And stopping the operation of the charging pile and notifying related personnel to carry out maintenance. And finally, storing the data through the cloud platform, and generating a broken line graph and a pie chart report. The system can efficiently and accurately monitor and evaluate the health state of the filter of the charging pile, and ensures stable operation and timely maintenance of the charging pile.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of charging piles, and particularly to an on-line fault detection method and system for charging piles. Background Art

[0002] With the popularization of electric vehicles, the charging infrastructure is also constantly developing and improving. As the core equipment in the charging process of electric vehicles, the reliability and stability of charging piles are crucial for ensuring the normal charging of vehicles. Filters are often equipped in charging piles, mainly used to suppress high-frequency noise and ripple in the power supply, ensure power quality, and avoid interference to surrounding electronic devices. Filters play an important role in ensuring the operation stability and power quality of charging piles. However, with the increase of service time, the performance of filters may gradually degrade, resulting in a decline in their filtering ability or even failure. Therefore, timely detection and evaluation of the health status of filters, especially on-line fault detection, become the key to ensuring the continuous and reliable operation of charging piles.

[0003] Existing filter fault detection methods usually rely on regular inspections or repairs after equipment failures. However, this passive maintenance method has great deficiencies. First, traditional monitoring methods cannot evaluate the performance of filters in real time and often rely on historical data for evaluation, which makes many fault problems not effectively identified in the early stage. Second, most existing diagnostic systems rely on limited sensor data, such as temperature, current, etc., and these parameters are not sufficient to comprehensively reflect the health status of filters. For example, bus voltage disturbances, dynamic changes in ripple, and high-frequency interference in the power supply module have important impacts on filter performance but are often ignored. More critically, the aging process of filters has complex and non-linear characteristics, and simple parameter monitoring is often difficult to accurately judge their failure risks. Based on these current situations and deficiencies, existing fault detection technologies often fail to provide sufficient warning time, resulting in equipment being processed only after failure, affecting the operation efficiency and equipment life of charging piles. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an on-line fault detection method and system for charging piles, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An on-line fault detection system for charging piles, including a data acquisition module, a ripple evolution evaluation module, a depth analysis module, a comprehensive health evaluation module, and an analysis report module The data acquisition module is used to continuously monitor the filter performance data of the charging pile according to the monitoring equipment installed in the charging pile, and transmit the filter performance data to the central control system through the Internet for preprocessing, so as to obtain a power quality data group, a harmonic characteristic data group, and a high-frequency interference data group; The ripple evolution evaluation module is used to calculate the ripple dynamic evolution index ahf based on the power quality data set and conduct filter health assessment; The in-depth analysis module is used to perform in-depth assessment when the filter health assessment indicates signs of aging. It calculates the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb based on the harmonic characteristic data set and the high-frequency interference data set respectively; The comprehensive health assessment module is used to comprehensively calculate the comprehensive health evolution index FHI based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl, and the high-frequency energy distribution response index xpb, and conduct comprehensive filter performance assessment; The analysis report module is used to align the preprocessed data and evaluation results according to the time stamp, store them in the cloud platform, and generate line chart and pie chart reports.

[0006] Preferably, the data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to monitor the filter performance data of the charging pile in real time according to the monitoring equipment installed on the charging pile; The monitoring equipment includes an oscilloscope, a patch thermocouple, a power analyzer, a spectrum analyzer, a high-bandwidth current probe, a high-frequency current probe, and a high-impedance voltage probe; Connect a high-bandwidth current probe to the return line of the positive pole of the power module bus, connect the other end of the high-bandwidth current probe to the oscilloscope, set the oscilloscope to AC coupling mode, and read the current ripple amplitude Ih through the oscilloscope; Connect a high-impedance voltage probe to the return line of the positive pole of the power module bus, connect the other end of the high-impedance voltage probe to the oscilloscope, set the oscilloscope to AC coupling mode, and read the voltage ripple amplitude vd through the oscilloscope; Collect the internal temperature nw of the power module in real time through the patch thermocouple installed on the surface of the internal components of the power module; Directly collect the harmonic component amplitudes sf of each order through a power analyzer; Connect a high-frequency current probe to the return line of the positive pole of the power module bus, connect the other end of the high-frequency current probe to the oscilloscope, and read the high-frequency ripple current value If in the bandpass region through the oscilloscope; Directly collect the main peak power gg of the high-frequency power spectral density through a spectrum analyzer.

[0007] Preferably, the data processing unit is used to transmit the collected filter performance data to the central control system through the Internet, and perform data standardization, data cleaning, and harmonic centroid analysis through the central control system to obtain the power quality data set, the harmonic characteristic data set, and the high-frequency interference data set; Data standardization is preprocessed by using the zero-mean normalization method to eliminate the dimensional differences between different parameters; data cleaning is performed by using missing value handling and outlier handling. Missing value handling fills the missing values in the dataset by using the mean filling method, and outlier handling detects and processes the outliers existing in the collected data group CMP by using the interquartile range method; Harmonic centroid analysis is based on the real-time acquisition of the bus current signal by a high-bandwidth current probe deployed on the output bus, and the fast Fourier transform (FFT) function of the oscilloscope is enabled to convert the collected time-domain current signal into a frequency-domain complex spectrum. Discrete frequency points f j and their corresponding amplitudes A(f j ) are extracted from the complex spectrum, and the harmonic energy centroid frequency xz is calculated through the frequency points f j and the amplitude A(f j ). Specifically, it is as follows: , where M represents the total number of effective frequency points obtained by dividing according to the frequency resolution within the set frequency range; The power quality data group includes the current ripple amplitude Ih, the internal temperature nw of the power module, and the voltage ripple amplitude vd; The harmonic characteristic data group includes the harmonic component amplitude sf and the harmonic energy centroid frequency xz; The high-frequency interference data group includes the high-frequency ripple current value If and the main peak power gg of the high-frequency power spectrum density.

[0008] Preferably, the ripple evolution evaluation module includes a dynamic evolution analysis unit and a ripple dynamic evaluation unit; The dynamic evolution analysis unit is used to perform summary calculations based on the obtained power quality data group to obtain the ripple dynamic evolution index ahf, which is used to describe the change in the local ripple energy intensity of the filter, and comprehensively analyze the health status of the filter in combination with temperature changes and bus voltage disturbances. The specific formula is as follows; ; In the formula, ∆t represents the sliding window time, exp represents the exponential decay function, Ih(t) represents the current ripple amplitude after high-frequency filtering at time t, nw ref represents the normal temperature standard reference temperature, set to 25 degrees, sin represents the sine function, k1 represents the temperature effect modulation coefficient, k2 represents the bus voltage disturbance modulation frequency factor, and dt represents the differential symbol in the integral formula.

[0009] Preferably, the ripple dynamic evaluation unit is used to sort the ripple dynamic evolution index ahf in the historical charging pile health state from small to large, determine the value of the ripple dynamic evolution index ahf at the 95% position by the P95 percentile method in statistics, set a preset ripple dynamic health threshold A based on the value at 95%, compare it with the real-time obtained ripple dynamic evolution index ahf, and then perform filter health assessment according to the comparison result. The specific assessment scheme is as follows; When the ripple dynamic evolution index ahf ≤ the ripple dynamic fluctuation threshold A, it indicates that the ripple dynamic change is normal and there is no sign of aging failure of the filter. At this time, normal monitoring is maintained; When the ripple dynamic evolution index ahf > the ripple dynamic fluctuation threshold A, it indicates that the ripple dynamic change is abnormal and there is a sign of aging failure of the filter. At this time, a depth assessment is performed.

[0010] Preferably, the depth analysis module is used to perform a depth assessment when the filter health assessment shows signs of aging; The depth assessment is used to perform a summary calculation based on the obtained harmonic characteristic data group and high-frequency interference data group, and respectively obtain the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb; The high-order harmonic comprehensive energy index xnl is used to analyze the high-order harmonic components output by the power supply, and analyze the high-frequency noise suppression ability of the filter through the monitoring of the total harmonic energy and frequency offset. The specific acquisition formula is as follows; ; In the formula, log represents the logarithmic function, n represents the order of the harmonic, sf n represents the amplitude of the nth harmonic component, cos represents the cosine function, f ref represents the harmonic frequency reference value under the standard state, represents pi, with two decimal places; The high-frequency energy distribution response index xpb is used to describe the comprehensive response characteristics of the internal high-frequency energy distribution and volatility of the charging pile filter under external environmental changes. The specific formula is as follows; ; In the formula, hr represents the environmental disturbance factor, N represents the total number of sampling points within the monitoring window, If i represents the high-frequency ripple current value at the i-th sampling point, represents the average value of the high-frequency ripple current within the window, and ln represents the logarithmic function.

[0011] Preferably, the comprehensive health assessment module includes a comprehensive health analysis unit and a health evolution assessment unit; The comprehensive health analysis unit is used to perform summary calculations based on the obtained ripple dynamic evolution index ahf, high-order harmonic comprehensive energy index xnl, and high-frequency energy distribution response index xpb to obtain the comprehensive health evolution index FHI. The specific formula is as follows; ; In the formula, ln represents the logarithmic function, and exp represents the exponential decay function.

[0012] Preferably, the health evolution evaluation unit is used to calculate the mean value of all historical comprehensive health evolution indexes FHI according to the statistical method, and set a preset comprehensive health threshold B based on the mean value. Then, it compares with the real-time obtained comprehensive health evolution index FHI, and evaluates the performance of the comprehensive filter based on the comparison result. The specific evaluation scheme is as follows; When the comprehensive health evolution index FHI ≤ the comprehensive health threshold B, it indicates that the filter performance is normal, and there is a misjudgment caused by external disturbances. At this time, it continues to run and increases the detection frequency by 50%; When the comprehensive health evolution index FHI > the comprehensive health threshold B, it indicates that the filter has a failure risk. At this time, a shutdown instruction is generated, and the central control system is used to control this charging pile to stop running, and relevant personnel are notified through the wireless network to repair and replace the filter of this charging pile.

[0013] Preferably, the analysis report module includes a data storage unit and a report display unit; The data storage unit is used to extract the power quality data group, harmonic characteristic data group, high-frequency interference data group, data analysis report, filter health assessment record, and comprehensive filter performance assessment record from the central control system in real time, record the acquisition timestamp of each data item, perform time alignment processing on the data according to the timestamp, and after uniformly classifying the data with the same timestamp and the analysis and evaluation results of the data with the same timestamp, transmit them to the cloud platform for storage; The report display unit is used to construct a line chart and a pie chart for display respectively based on the data and evaluation results stored in the cloud platform, and automatically generate a report according to the set period. When the evaluation result is that the ripple dynamic change is abnormal and the filter has a failure risk, a report is immediately generated and transmitted to relevant personnel through the Internet; The line chart is used to display the change trend of time series data over time; The pie chart is used to display the proportion of the device health status, that is, the proportion of the evaluation results of abnormal ripple dynamic changes and filter failure risks.

[0014] An on-line fault detection method for a charging pile includes the following steps: S1. Monitor the filtering performance data of the charging pile in real time based on the monitoring equipment installed on the charging pile, and transmit the filtering performance data to the central control system through the Internet for preprocessing to obtain the power quality data set, harmonic characteristic data set, and high-frequency interference data set; S2. Calculate the ripple dynamic evolution index ahf based on the power quality data set and conduct a health assessment of the filter; S3. When the health assessment of the filter indicates signs of aging, perform a depth assessment, and calculate the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb based on the harmonic characteristic data set and the high-frequency interference data set respectively; S4. Conduct a comprehensive calculation of the comprehensive health evolution index FHI based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl, and the high-frequency energy distribution response index xpb, and perform a comprehensive filter performance assessment; S5. After aligning the preprocessed data and evaluation results according to the time stamp, store them in the cloud platform and generate line chart and pie chart reports.

[0015] The present invention provides an on-line fault detection method and system for a charging pile. It has the following beneficial effects: (1) The data acquisition module of the system collects the filtering performance data of the charging pile in real time through the monitoring equipment. The monitoring equipment includes an oscilloscope, a patch thermocouple, a power analyzer, a spectrum analyzer, a high-bandwidth current probe, a high-frequency current probe, and a high-impedance voltage probe, and transmits the filtering performance data to the central control system through the Internet for preprocessing to obtain the power quality data set, the harmonic characteristic data set, and the high-frequency interference data set, providing an accurate data basis for subsequent health assessment and fault diagnosis.

[0016] (2) The ripple evolution evaluation module of the system calculates the ripple dynamic evolution index ahf based on the power quality data set, which can describe the change in the local ripple energy intensity of the filter, and conducts a comprehensive analysis in combination with the temperature change and the disturbance of the bus voltage to evaluate the health status of the filter. Through dynamic evolution analysis and ripple dynamic evaluation, this module can timely detect whether there are signs of aging in the filter. When the health assessment of the filter indicates signs of aging, the depth analysis module performs a depth assessment and calculates the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb based on the harmonic characteristic data set and the high-frequency interference data set respectively. Through the analysis of these two indicators, the system can accurately evaluate the high-frequency noise suppression ability of the filter and the response characteristics to external disturbances.

[0017] (3) The comprehensive health assessment module of the system calculates the summary based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl, and the high-frequency energy distribution response index xpb to obtain the comprehensive health evolution index FHI, and evaluates the filter performance with the preset comprehensive health threshold B. If the value of the comprehensive health evolution index FHI exceeds the preset comprehensive health threshold B, the system will trigger a shutdown instruction and notify relevant personnel through the wireless network for the inspection and replacement of the filter. The analysis report module, through the data storage unit and the report display unit, aligns the data according to the time stamp, generates line charts and pie chart reports, and displays the proportion of the equipment health status, abnormal ripple dynamic changes, and filter failure risks, ensuring the efficient and real-time progress of the fault monitoring and evaluation work of the charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flowchart of an on-line fault detection system for a charging pile according to the present invention; Figure 2 It is a schematic diagram of the steps of an on-line fault detection method for a charging pile according to the present invention; Figure 3 It is a schematic diagram of the integral interval of the ripple dynamic evolution index ahf formula according to the present invention; Figure 4 It is a schematic diagram of the operating principle architecture of an on-line fault detection system for a charging pile according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1 Please refer to Figure 1 , the present invention provides an on-line fault detection system for a charging pile. To achieve the above objectives, the present invention is implemented through the following technical solutions: including a data acquisition module, a ripple evolution evaluation module, a depth analysis module, a comprehensive health assessment module, and an analysis report module The data acquisition module is used to continuously monitor the filter performance data of the charging pile according to the monitoring equipment installed on the charging pile, and transmit the filter performance data to the central control system through the Internet for preprocessing to obtain a power quality data group, a harmonic characteristic data group, and a high-frequency interference data group; The ripple evolution evaluation module is used to calculate the ripple dynamic evolution index ahf based on the power quality data group for filter health assessment; The deep analysis module is used to perform a deep evaluation when the filter health assessment indicates signs of aging. It calculates the high - order harmonic comprehensive energy index xnl and the high - frequency energy distribution response index xpb based on the harmonic characteristic data set and the high - frequency interference data set respectively. The comprehensive health assessment module is used to comprehensively calculate the comprehensive health evolution index FHI based on the ripple dynamic evolution index ahf, the high - order harmonic comprehensive energy index xnl, and the high - frequency energy distribution response index xpb, and perform a comprehensive filter performance assessment. The analysis report module is used to align the pre - processed data and the evaluation results according to the time stamp, store them in the cloud platform, and generate line chart and pie chart reports.

[0021] In this embodiment, the data acquisition module is based on the monitoring equipment installed on the charging pile, and it obtains the filtering performance data in real time and transmits it to the central control system via the Internet for preprocessing, obtaining the power quality data set, the harmonic characteristic data set, and the high-frequency interference data set, ensuring the real-time transmission and accuracy of key data such as power quality, harmonic characteristics, and high-frequency interference. These data provide a solid foundation for the subsequent filter health assessment and fault prediction, making the system superior to traditional solutions in terms of the accuracy and timeliness of data acquisition. The ripple evolution assessment module calculates the ripple dynamic evolution index ahf based on the power quality data set and conducts a filter health assessment, thereby timely evaluating the health status of the filter and detecting possible aging signs in advance. Compared with traditional static assessment methods, this module can capture subtle changes in filter performance in real time through dynamic analysis and continuous monitoring, thus providing a more accurate health status assessment. The in-depth analysis module further analyzes the harmonic characteristic and high-frequency interference data when the filter assessment shows aging signs, calculates the comprehensive high-order harmonic energy index xnl and the high-frequency energy distribution response index xpb, further improving the sensitivity of fault detection, enabling the system to identify more complex filter problems, and enhancing the accuracy of fault warning and the ability of early prediction. The comprehensive health assessment module generates the comprehensive health evolution index FHI through comprehensive calculation of the ripple dynamic evolution index ahf, the comprehensive high-order harmonic energy index xnl, and the high-frequency energy distribution response index xpb, and conducts a comprehensive filter performance assessment, providing a comprehensive performance assessment for the filter. This comprehensive assessment method has more advantages than traditional single-index assessment schemes, can comprehensively understand the operating status of the filter from multiple perspectives, and avoids missed diagnosis and misjudgment. The analysis report module generates line chart and pie chart reports in real time through data alignment and cloud platform storage, further enhancing the visualization and operability of fault monitoring, improving the work efficiency of maintenance personnel, being able to respond to filter performance degradation problems in a timely manner, and avoiding potential safety hazards caused by equipment failures. This series of improvements not only improves the reliability and safety of the charging pile, but also provides a more accurate tool for the preventive maintenance of power equipment, greatly enhancing the operation efficiency and maintenance effect of the charging facilities.

[0022] Embodiment 2 Please refer to Figure 1 , specifically: The data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to monitor the filtering performance data of the charging pile in real time based on the monitoring equipment installed on the charging pile; The monitoring equipment includes an oscilloscope, a patch thermocouple, a power analyzer, a spectrum analyzer, a high-bandwidth current probe, a high-frequency current probe, and a high-impedance voltage probe; By connecting a high-bandwidth current probe to the return line of the positive pole of the power module bus, connecting the other end of the high-bandwidth current probe to an oscilloscope, setting the oscilloscope to AC coupling mode, and reading the current ripple amplitude Ih through the oscilloscope, the high-frequency oscillation intensity of the power output current during the charging process is reflected; By connecting a high-impedance voltage probe to the return line of the positive pole of the power module bus, connecting the other end of the high-impedance voltage probe to an oscilloscope, setting the oscilloscope to AC coupling mode, and reading the voltage ripple amplitude vd through the oscilloscope, the high-frequency voltage fluctuation amplitude on the DC bus is represented, that is, the voltage change not absorbed by the front-stage PFC and filter capacitor, and the periodic influence of the bus voltage disturbance on the ripple fluctuation is analyzed; By using surface-mounted patch thermocouples on the internal components of the power module to collect the internal temperature nw of the power module in real time, which represents the operating temperature inside the power module and reflects the thermal environment of the module operation; By directly collecting the harmonic component amplitudes sf of each order through a power analyzer, which refers to the current amplitudes of specific high-order harmonics, including the current amplitudes of the 5th, 7th, 11th, and 13th harmonics, reflecting the magnitude of high-order distortion in the output current and used to describe the degradation of the high-frequency harmonic suppression ability of the filter; By connecting a high-frequency current probe to the return line of the positive pole of the power module bus, connecting the other end of the high-frequency current probe to an oscilloscope, and reading the high-frequency ripple current value If in the band-pass region through the oscilloscope, the high-frequency interference suppression ability of the filter is reflected, and the driving variables for early degradation and dynamic trend analysis are identified; By directly collecting the main peak power gg of the high-frequency power spectral density through a spectrum analyzer, which represents the frequency energy in the spectrum and reflects the degree of abnormal concentration of energy in a certain frequency band after the filter fails.

[0023] The data processing unit is used to transmit the collected filter performance data to the central control system through the Internet, and perform data standardization, data cleaning, and harmonic centroid analysis through the central control system to obtain the power quality data set, harmonic characteristic data set, and high-frequency interference data set; Data standardization is preprocessed by using the zero-mean normalization method to eliminate the dimension differences between different parameters; data cleaning is performed by using missing value processing and outlier processing. Missing value processing fills the missing values in the data set by using the mean filling method, and outlier processing detects and processes the outliers existing in the collected data set CMP by using the interquartile range method; Harmonic centroid analysis is based on the real-time collection of the bus current signal by a high-bandwidth current probe arranged on the output bus, and by enabling the fast Fourier transform FFT function through the oscilloscope, the collected time-domain current signal is converted into a frequency-domain complex spectrum, and the discrete frequency points f j and their corresponding amplitudes A(fj ) and obtain the harmonic energy centroid frequency xz through the frequency point f j and the amplitude A(f j ), which is used to measure the deviation trend of harmonic distribution. Specifically: , where M represents the total number of effective frequency points obtained by dividing according to the frequency resolution within the set frequency range; The power quality data set includes the current ripple amplitude Ih, the internal temperature nw of the power supply module, and the voltage ripple amplitude vd; The harmonic characteristic data set includes the harmonic component amplitude sf and the harmonic energy centroid frequency xz; The high-frequency interference data set includes the high-frequency ripple current value If and the main peak power gg of the high-frequency power spectrum density.

[0024] In this embodiment, the data acquisition unit comprehensively acquires the filter performance data of the charging pile through monitoring devices such as an oscilloscope, a patch thermocouple, a power analyzer, a spectrum analyzer, a high-bandwidth current probe, a high-frequency current probe, and a high-impedance voltage probe. The data processing unit transmits the acquired filter performance data to the central control system through the Internet and performs preprocessing using methods such as zero-mean normalization, missing value filling, and outlier processing to ensure the accuracy and consistency of the data. The harmonic centroid analysis method further extracts the harmonic energy centroid frequency to help accurately evaluate the filter performance, and finally obtains the power quality data set, the harmonic characteristic data set, and the high-frequency interference data set. The implementation of this system significantly improves the real-time performance and accuracy of charging pile fault detection, can effectively monitor the health status of the filter, identify potential faults in advance, and provide strong data support for the maintenance and optimization of the charging pile, avoiding safety hazards caused by equipment failures and enhancing the reliability and operation efficiency of the charging pile.

[0025] Embodiment 3 Please refer to Figure 1 and Figure 3 , specifically: The ripple evolution evaluation module includes a dynamic evolution analysis unit and a ripple dynamic evaluation unit; The dynamic evolution analysis unit is used to perform summary calculations based on the obtained power quality data set to obtain the ripple dynamic evolution index ahf, which is used to describe the change in the local ripple energy intensity of the filter, and comprehensively analyze the health status of the filter in combination with temperature changes and bus voltage disturbances. The specific formula is as follows; ; In the formula, ∆t represents the sliding window time, exp represents the exponential decay function, Ih(t) represents the current ripple amplitude after high-frequency filtering at time t, nw refDenote the normal temperature standard reference temperature, set at 25 degrees Celsius, which is used to measure the impact of temperature deviation on the acceleration of degradation. "sin" represents the sine function, and k1 represents the temperature effect modulation coefficient, which is set by the user according to the actual situation and is used to control the sensitivity of temperature changes in the exponential function. k2 represents the bus voltage disturbance modulation frequency factor, which is set by the user based on the typical ripple frequency of the bus. "dt" represents the differential symbol in the integral formula. Denote the local short-term energy density. Taking the square root converts the energy to amplitude, which is used to capture the local intensity fluctuations of the ripple. Denote the exponential modulation function, which modulates the impact of temperature on the degradation rate through an exponential curve form. By inputting the bus voltage ripple amplitude into the sine function, the impact of periodic disturbances is simulated.

[0026] The ripple dynamic evaluation unit is used to sort the ripple dynamic evolution index ahf in the historical charging pile health state from small to large, determine the value of the ripple dynamic evolution index ahf at the 95% position through the P95 percentile method in statistics, preset the ripple dynamic health threshold A based on the value at 95%, compare it with the real-time obtained ripple dynamic evolution index ahf, and then evaluate the health of the filter according to the comparison result. The specific evaluation scheme is as follows; When the ripple dynamic evolution index ahf ≤ the ripple dynamic fluctuation threshold A, it indicates that the ripple dynamic change is normal and there are no signs of aging failure in the filter. At this time, normal monitoring is maintained; When the ripple dynamic evolution index ahf > the ripple dynamic fluctuation threshold A, it indicates that the ripple dynamic change is abnormal and there are signs of aging failure in the filter. At this time, a depth evaluation is performed.

[0027] In this embodiment, the dynamic evolution analysis unit calculates the ripple dynamic evolution index ahf, combines temperature changes and bus voltage disturbances, comprehensively analyzes the changes in the local ripple energy intensity of the filter, and reflects the health status of the filter in real time. This method accurately captures the accelerating effects of temperature deviation and voltage disturbance on the degradation of the filter through an exponential decay function, a sine function, and multiple modulation factors, improving the sensitivity of fault detection and the early warning ability. The ripple dynamic evaluation unit sorts the ripple dynamic evolution index ahf in the historical charging pile health state from small to large, sets the ripple dynamic health threshold A through the statistical P95 percentile method, and compares and evaluates in real time whether the ripple dynamic change is normal, thereby realizing the health status evaluation of the filter. When abnormal ripple dynamic changes are detected, the system triggers a depth evaluation to further diagnose whether there are aging failures in the filter, ensuring that potential problems of the equipment can be processed in a timely manner. Through this real-time and accurate dynamic evaluation method, the system greatly improves the accuracy and response speed of the charging pile filter fault warning, significantly enhancing the maintenance efficiency and operation safety of the equipment.

[0028] Example 4 Please refer to Figure 1 , specifically: the in-depth analysis module is used to perform in-depth evaluation when the filter health assessment indicates signs of aging; The in-depth evaluation is used to perform summary calculations based on the obtained harmonic characteristic data set and high-frequency interference data set, and respectively obtain the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb; The high-order harmonic comprehensive energy index xnl is used to analyze the high-order harmonic components of the power supply output, and analyze the high-frequency noise suppression ability of the filter by monitoring the total harmonic energy and frequency offset. The specific acquisition formula is as follows; ; In the formula, log represents the logarithmic function, n represents the order of the harmonic, sf n represents the amplitude of the nth harmonic component, cos represents the cosine function, f ref represents the harmonic frequency reference value under the standard state, represents pi, with two decimal places, is used to perform logarithmic compression on the total high-order harmonic energy, represents the sum of the energies of the 5th, 7th, 11th, and 13th harmonics, By multiplying the frequency ratio by mapping to the angular system, and then reflecting the cosine phase change caused by frequency drift through the cosine function; The high-frequency energy distribution response index xpb is used to describe the comprehensive response characteristics of the internal high-frequency energy distribution and volatility of the charging pile filter under external environmental changes. The specific formula is as follows; ; In the formula, hr represents the environmental disturbance factor, which is set by the user according to the actual situation and reflects the interference sensitivity of the external environment to the internal operation of the charging pile. N represents the total number of sampling points within the monitoring window, If i represents the high-frequency ripple current value at the i-th sampling point, represents the average high-frequency ripple current within the window, ln represents the logarithmic function, represents the standard deviation of the high-frequency ripple change, Adjust the influence of the abnormal increase in the main peak power of the high-frequency power spectrum density through the logarithmic scale to avoid the amplification of single-point anomalies.

[0029] In this embodiment, when the depth analysis module determines that there are signs of aging in the filter health assessment, it performs in-depth fault analysis. By calculating the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb from the harmonic characteristic data group and the high-frequency interference data group, it further evaluates the filter's ability to suppress high-frequency noise and its response ability to external environmental changes. Specifically, when implemented, the high-order harmonic comprehensive energy index xnl accurately measures the filter's noise suppression ability by analyzing the high-order harmonic components of the power supply output, combined with the total harmonic energy and frequency offset; the high-frequency energy distribution response index xpb reflects the high-frequency energy distribution and volatility response of the filter under external environmental disturbances, avoiding error amplification caused by external interference. Through this in-depth analysis, the system can timely identify and locate minor changes in the filter performance, improving the accuracy of fault prediction. Compared with the traditional single-index evaluation method, the combination of multiple levels of analysis significantly improves the detection ability for complex faults, optimizes the fault warning system of the charging pile, reduces the equipment's fault risk and maintenance cost, thereby enhancing the overall stability and reliability of the system.

[0030] Embodiment 5 Please refer to Figure 1 , specifically: The comprehensive health assessment module includes a comprehensive health analysis unit and a health evolution assessment unit; The comprehensive health analysis unit is used to perform summary calculations based on the obtained ripple dynamic evolution index ahf, high-order harmonic comprehensive energy index xnl, and high-frequency energy distribution response index xpb to obtain the comprehensive health evolution index FHI. The specific formula is as follows; ; In the formula, ln represents the logarithmic function, and exp represents the exponential decay function. The high-order harmonic comprehensive energy index is subjected to square and logarithmic composite processing to analyze the non-linear amplification characteristics of the internal deterioration intensity coupling of the filter. The high-frequency energy distribution response index is amplified through the exponential decay function to analyze the response amplification degree of the filter to external disturbance changes.

[0031] 8. A charging pile fault online detection system according to claim 7, characterized in that: The health evolution assessment unit is used to calculate the mean value of all historical comprehensive health evolution indexes FHI according to the statistical method, and set a preset comprehensive health threshold B based on the mean value, and then compare it with the real-time obtained comprehensive health evolution index FHI. Based on the comparison result, the comprehensive filter performance is evaluated. The specific evaluation scheme is as follows; When the comprehensive health evolution index FHI ≤ the comprehensive health threshold B, it indicates that the filter performance is normal and there is a misjudgment caused by external disturbance. At this time, continue to run and increase the detection frequency by 50%; When the comprehensive health evolution index FHI > the comprehensive health threshold B, it indicates that there is a risk of filter failure. At this time, a shutdown instruction is generated, and this charging pile is controlled to stop running through the central control system, and relevant personnel are notified through the wireless network to repair and replace the filter of this charging pile.

[0032] In this embodiment, the comprehensive health assessment module realizes the comprehensive monitoring and accurate evaluation of the performance of the charging pile filter through the collaborative work of the comprehensive health analysis unit and the health evolution assessment unit. The comprehensive health analysis unit performs summary calculations based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl, and the high-frequency energy distribution response index xpb to obtain the comprehensive health evolution index FHI. Through the processing of complex logarithmic and exponential decay functions, it can deeply analyze the deterioration intensity inside the filter and the response amplification effect to external disturbances. This process effectively reveals the non-linear degradation characteristics of the filter and identifies potential failure risks in advance. The health evolution assessment unit calculates the mean value of the historical comprehensive health evolution index FHI and sets the comprehensive health threshold B for the comprehensive filter performance assessment. When the comprehensive health evolution index FHI exceeds the comprehensive health threshold B, the system will automatically generate a shutdown instruction and notify relevant personnel for timely repair and replacement through the wireless network, thus avoiding sudden equipment failures and unnecessary downtime. Through the implementation of this module, the fault warning of the charging pile and the performance assessment of the filter have been significantly improved, not only improving the reliability and safety of the charging pile, but also enhancing the maintenance efficiency of the equipment and the intelligent level of operation management.

[0033] Embodiment 6 Please refer to Figure 1 , specifically: The analysis report module includes a data storage unit and a report display unit; The data storage unit is used to extract the power quality data group, harmonic characteristic data group, high-frequency interference data group, data analysis report, filter health assessment record, and comprehensive filter performance assessment record from the central control system in real time, record the acquisition timestamp of each data item, perform time alignment processing on the data according to the timestamp, and after uniformly classifying the data with the same timestamp and the analysis and evaluation results of the data with the same timestamp, transmit them to the cloud platform for storage; The report display unit is used to construct a line chart and a pie chart for display respectively based on the data and evaluation results stored in the cloud platform, and automatically generate a report according to the set period. When the evaluation result is that the ripple dynamic change is abnormal and the filter has a risk of failure, a report will be generated immediately and transmitted to relevant personnel through the Internet; The line chart is used to display the change trend of time series data over time; The pie chart is used to display the proportion of the equipment health status, that is, the proportion of the evaluation results of abnormal ripple dynamic change and the filter having a risk of failure.

[0034] In this embodiment, the analysis report module realizes the comprehensive monitoring and timely feedback of the health status of the charging pile filter through the collaborative work of the data storage unit and the report display unit. The data storage unit extracts the power quality, harmonic characteristics, high-frequency interference data set, and health assessment records from the central control system in real time, aligns the data based on the time stamp, and transmits them to the cloud platform for storage after unified classification, ensuring the integrity and timeliness of all data. The report display unit automatically generates and displays line charts and pie charts based on the data stored in the cloud platform, intuitively presenting the device health status and the change trend of the time series. Especially when there is a risk of filter failure, the system can quickly generate and transmit reports to relevant personnel. Through this automated and real-time data analysis and report generation method, the system effectively improves the fault warning ability and operation and maintenance efficiency of the charging pile, ensures the reliability of the filter and the continuous operation of the device, reduces the burden of manual monitoring, optimizes the fault response speed, and thus improves the overall operation efficiency and safety of the charging pile.

[0035] Embodiment 7 Please refer to Figure 2 , an on-line fault detection method for a charging pile, comprising the following steps: S1. According to the monitoring equipment installed on the charging pile, the filtering performance data of the charging pile is monitored in real time, and the filtering performance data is transmitted to the central control system through the Internet for preprocessing to obtain a power quality data set, a harmonic characteristic data set, and a high-frequency interference data set; S2. Calculate the ripple dynamic evolution index ahf based on the power quality data set for filter health assessment; S3. When the filter health assessment shows signs of aging, perform a depth assessment, and calculate the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb based on the harmonic characteristic data set and the high-frequency interference data set respectively; S4. Perform a comprehensive calculation of the comprehensive health evolution index FHI based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl, and the high-frequency energy distribution response index xpb for comprehensive filter performance evaluation; S5. After aligning the preprocessed data and evaluation results based on the time stamp, store them in the cloud platform, and generate line charts and pie chart reports.

[0036] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirits of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A charging pile fault online detection system, characterized by: Includes data acquisition module, ripple evolution assessment module, in-depth analysis module, comprehensive health assessment module and analysis report module The data acquisition module is used to monitor the filter performance data of the charging pile in real time based on the monitoring equipment installed on the charging pile, and transmit the filter performance data to the central control system through the Internet for preprocessing to obtain power quality data group, harmonic characteristic data group and high-frequency interference data group; The ripple evolution assessment module is used to calculate the ripple dynamic evolution index ahf based on the power quality data group to perform filter health assessment; The deep analysis module is used to perform a deep assessment when the filter health assessment shows signs of aging, and calculate the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb respectively based on the harmonic characteristic data group and the high-frequency interference data group; The comprehensive health assessment module is used to comprehensively calculate the comprehensive health evolution index FHI based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb, and conduct a comprehensive filter performance assessment; The analysis report module is used to align the preprocessed data and evaluation results according to the timestamp, store them in the cloud platform, and generate line graph and pie chart reports.

2. The charging pile fault online detection system according to claim 1, characterized in that: The data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to monitor the filtering performance data of the charging pile in real time based on the monitoring equipment installed on the charging pile; Monitoring equipment includes oscilloscopes, chip thermocouples, power analyzers, spectrum analyzers, high-bandwidth current probes, high-frequency current probes, and high-impedance voltage probes; Connect a high-bandwidth current probe to the return line of the positive pole of the power module busbar, connect the other end of the high-bandwidth current probe to an oscilloscope, set the oscilloscope to AC coupling mode, and read the current ripple amplitude Ih through the oscilloscope; Connect a high-impedance voltage probe to the return line of the positive pole of the power module busbar, connect the other end of the high-impedance voltage probe to an oscilloscope, set the oscilloscope to AC coupling mode, and read the voltage ripple amplitude vd through the oscilloscope; The internal temperature nw of the power module is collected in real time through the patch thermocouple installed on the surface of the internal components of the power module; The amplitude sf of harmonic components of each order is directly collected through the power analyzer; Connect a high-frequency current probe to the return line of the positive pole of the power module busbar, and connect the other end of the high-frequency current probe to an oscilloscope, and read the high-frequency ripple current value If in the passband region through the oscilloscope; The main peak power gg of the high-frequency power spectrum density is directly collected by a spectrum analyzer.

3. The charging pile fault online detection system according to claim 2 is characterized in that: The data processing unit is used to transmit the collected filtering performance data to the central control system through the Internet, and perform data standardization, data cleaning and harmonic centroid analysis through the central control system to obtain power quality data groups, harmonic characteristic data groups and high-frequency interference data groups; Data standardization is performed by preprocessing using the zero mean standardization method to eliminate the dimensional differences between different parameters; Data cleaning is performed by using missing value processing and outlier processing. Missing value processing uses the mean imputation method to fill in the missing values ​​in the data set. Outlier processing uses the interquartile range method to detect and process outliers in the collected data set CMP. Harmonic centroid analysis is based on real-time acquisition of bus current signals by high-bandwidth current probes placed on the output bus, and the fast Fourier transform (FFT) function is enabled on the oscilloscope to convert the acquired time-domain current signals into frequency-domain complex spectrum, from which discrete frequency points f are extracted. j and its corresponding amplitude A(f j ), and through the frequency point f j and amplitude A(f j ) Calculate and obtain the harmonic energy centroid frequency xz, specifically: , where M represents the total number of valid frequency points obtained by dividing according to the frequency resolution within the set frequency range; The power quality data group includes the current ripple amplitude Ih, the power module internal temperature nw and the voltage ripple amplitude vd; The harmonic characteristic data set includes the harmonic component amplitude sf and the harmonic energy centroid frequency xz; The high-frequency interference data group includes the high-frequency ripple current value If and the high-frequency power spectrum density main peak power gg.

4. The charging pile fault online detection system according to claim 3 is characterized in that: The ripple evolution assessment module includes a dynamic evolution analysis unit and a ripple dynamic assessment unit; The dynamic evolution analysis unit is used to perform summary calculations based on the acquired power quality data group to obtain the ripple dynamic evolution index ahf, which is used to describe the change in the local ripple energy intensity of the filter, and comprehensively analyze the health status of the filter in combination with the temperature change and the bus voltage disturbance. The specific formula is as follows; ； Where ∆t represents the sliding window time, exp represents the exponential decay function, Ih(t) represents the current ripple amplitude after high-frequency filtering at time t, and nw ref represents the standard reference temperature of room temperature, which is set to 25 degrees, sin represents the sine function, k1 represents the temperature effect modulation coefficient, k2 represents the bus voltage disturbance modulation frequency factor, and dt represents the differential sign in the integral.

5. The charging pile fault online detection system according to claim 4 is characterized in that: The ripple dynamic evaluation unit is used to sort the ripple dynamic evolution index ahf under the health status of the historical charging pile from small to large, and determine the value of the ripple dynamic evolution index ahf at the 95% position through the P95 quantile method in statistics, and preset the ripple dynamic health threshold A based on the value at 95%, and compare it with the ripple dynamic evolution index ahf obtained in real time, and then perform filter health evaluation based on the comparison result. The specific evaluation plan is as follows; When the ripple dynamic evolution index ahf ≤ the ripple dynamic fluctuation threshold A, it means that the ripple dynamic change is normal and there is no sign of aging failure in the filter. At this time, normal monitoring is maintained; When the ripple dynamic evolution index ahf> the ripple dynamic fluctuation threshold A, it means that the ripple dynamic change is abnormal and the filter has signs of aging failure. At this time, a deep evaluation is performed.

6. A charging pile fault online detection system according to claim 5, characterized in that: The deep analysis module is used to perform a deep assessment when the filter health assessment shows signs of aging; The depth evaluation is used to perform summary calculations based on the acquired harmonic characteristic data group and high-frequency interference data group, and obtain the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb respectively; The high-order harmonic comprehensive energy index xnl is used to analyze the high-order harmonic components of the power supply output, and analyze the filter's ability to suppress high-frequency noise by monitoring the total amount of harmonic energy and frequency deviation. The specific acquisition formula is as follows; ； In the formula, log represents the logarithmic function, n represents the order of the harmonic, and sf n represents the amplitude of the nth harmonic component, cos represents the cosine function, f ref Indicates the harmonic frequency reference value under standard conditions, Indicates pi, with two decimal places; The high-frequency energy distribution response index xpb is used to describe the comprehensive response characteristics of the internal high-frequency energy distribution and volatility of the charging pile filter under changes in the external environment. The specific formula is as follows; ； In the formula, hr represents the environmental disturbance factor, N represents the total number of sampling points in the monitoring window, and If i represents the high-frequency ripple current value at the i-th sampling point, It represents the average value of high-frequency ripple current in the window, and ln represents the logarithmic function.

7. A charging pile fault online detection system according to claim 6, characterized in that: The comprehensive health assessment module includes the comprehensive health analysis unit and the health evolution assessment unit; The comprehensive health analysis unit is used to perform summary calculation based on the acquired ripple dynamic evolution index ahf, high-order harmonic comprehensive energy index xnl and high-frequency energy distribution response index xpb to obtain the comprehensive health evolution index FHI. The specific formula is as follows; ； Where ln represents the logarithmic function and exp represents the exponential decay function.

8. The charging pile fault online detection system according to claim 7, characterized in that: The health evolution evaluation unit is used to calculate the mean of all historical comprehensive health evolution indexes FHI according to the statistical method, and preset the comprehensive health threshold B based on the mean, and then compare it with the comprehensive health evolution index FHI obtained in real time, and perform comprehensive filter performance evaluation based on the comparison result. The specific evaluation scheme is as follows; When the comprehensive health evolution index FHI ≤ the comprehensive health threshold B, it means that the filter performance is normal and there is a misjudgment caused by external disturbance. At this time, the filter continues to run and the detection frequency is increased by 50%; When the comprehensive health evolution index FHI is greater than the comprehensive health threshold B, it indicates that the filter is at risk of failure. At this time, a shutdown command is generated, the charging pile is controlled to stop running through the central control system, and relevant personnel are notified through the wireless network to inspect and replace the charging pile filter.

9. The charging pile fault online detection system according to claim 1, characterized in that: The analysis report module includes a data storage unit and a report display unit; The data storage unit is used to extract power quality data groups, harmonic characteristic data groups, high-frequency interference data groups, data analysis reports, filter health assessment records and comprehensive filter performance assessment records from the central control system in real time, and record the acquisition timestamp of each data item, perform time alignment processing on the data according to the timestamp, and classify the data with the same timestamp and the analysis and assessment results of the data with the same timestamp uniformly, and then transmit them to the cloud platform for storage; The report display unit is used to construct line charts and pie charts for display based on the data and evaluation results stored in the cloud platform, and automatically generate reports according to the set cycle. When the evaluation results show that the ripple dynamic change is abnormal and the filter has a risk of failure, the report is immediately generated and transmitted to relevant personnel via the Internet; Line charts are used to show the changing trend of time series data over time; The pie chart is used to show the proportion of equipment health status, that is, the proportion of evaluation results showing abnormal ripple dynamic changes and filter failure risks.

10. A charging pile fault online detection method, applied to a charging pile fault online detection system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Monitor the filter performance data of the charging pile in real time according to the monitoring equipment installed on the charging pile, and transmit the filter performance data to the central control system through the Internet for preprocessing to obtain a power quality data group, a harmonic characteristic data group and a high-frequency interference data group; S2. Calculate the ripple dynamic evolution index ahf based on the power quality data group to perform filter health assessment; S3. When the filter health assessment shows signs of aging, perform a deep assessment and calculate the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb respectively based on the harmonic characteristic data group and the high-frequency interference data group; S4. Comprehensively calculate the comprehensive health evolution index FHI based on the ripple dynamic evolution index ahf, the high-order harmonic comprehensive energy index xnl and the high-frequency energy distribution response index xpb to conduct a comprehensive filter performance evaluation; S5. After aligning the preprocessed data and evaluation results according to the timestamp, the data is stored in the cloud platform, and a line chart and pie chart report is generated.

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