CN118944252B - Charging detection method, device, equipment and storage medium of charger - Google Patents
Charging detection method, device, equipment and storage medium of charger Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention provides a charging detection method, a device, equipment and a storage medium of a charger, wherein the method comprises the step of acquiring starting and dynamic charging current and voltage data through a harmonic electric energy meter in the starting and charging process of a charging pile. The management system performs initial harmonic characteristic analysis on the starting current and the starting voltage to generate a reference harmonic characteristic data set, and performs dynamic harmonic characteristic analysis on the dynamic current and the dynamic voltage to generate a dynamic harmonic characteristic data set. And carrying out abnormality detection on the working state of the charging pile based on the reference, the dynamic harmonic data and the preset normal harmonic data, and generating a grading abnormality alarm signal. And making an intelligent decision according to the alarm signal, sending a charging control instruction, and dynamically adjusting the charging operation of the charging pile. According to the invention, through analyzing the harmonic characteristics of the charging current and the voltage, the abnormal state in the charging process is accurately detected, the abnormal detection precision is improved, the adjustment is made according to different abnormal conditions, and the safety and the reliability of the charging operation are improved.
Description
Technical Field
The present invention relates to the field of charging technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting charging of a charger.
Background
With the rapid development of the electric automobile industry, the charging pile is used as a key infrastructure for charging the electric automobile, and the demand is increasing. In the charging process, how to ensure the efficient operation, the safety reliability and the fault detection capability of the charging pile becomes a core problem for ensuring the normal charging of the electric automobile. Conventional charging detection methods generally rely on simple current and voltage monitoring means, and it is difficult to accurately identify abnormal conditions in the charging process, such as current fluctuation, overload, harmonic interference, and the like, which may cause charging faults or potential safety hazards. During the charging process, the current and voltage may generate different levels of harmonic interference due to the influence of equipment, environment, and the like. The presence of harmonics not only affects the operating state of the charging device, but may also negatively affect the power grid. Therefore, how to accurately analyze and detect the harmonic waves becomes an important technical means for improving the safety and stability of the charging equipment. However, existing charger detection systems often lack the ability to analyze complex harmonic features, failing to provide intelligent decision support for abnormal situations, resulting in less efficient charge management.
Disclosure of Invention
The invention mainly aims to solve the technical problems that the detection of the existing charger lacks the analysis capability of complex harmonic characteristics, intelligent decision support for abnormal conditions cannot be provided, and the charging management efficiency is low;
the first aspect of the invention provides a charging detection method of a charger, the charger comprises a single-way single-plug charging pile, a harmonic electric energy meter and a management system, and the charging detection method of the charger comprises the following steps:
when the charging operation of the single-channel single-plug charging pile is started and the charging operation is performed, acquiring starting charging current and starting charging voltage, dynamic charging current and dynamic charging voltage of the single-channel single-plug charging pile through the harmonic electric energy meter;
The management system performs initial harmonic characteristic analysis processing on the starting charging current and the starting charging voltage to obtain a reference harmonic characteristic data set, and performs dynamic harmonic characteristic analysis processing on the dynamic charging current and the dynamic charging voltage to obtain a dynamic harmonic characteristic data set;
performing abnormality detection processing on the working state of the single-path single-plug charging pile by the management system according to the reference harmonic characteristic data set, the dynamic harmonic characteristic data set and preset normal harmonic data to obtain a grading abnormality alarm signal;
and carrying out intelligent decision processing on the charging operation of the single-channel single-plug charging pile according to the grading abnormal alarm signal by the management system to obtain a charging control instruction, and adjusting the charging operation of the single-channel single-plug charging pile according to the charging control instruction.
Optionally, in a first implementation manner of the first aspect of the present invention, the performing, by the management system, initial harmonic characteristic analysis processing on the starting charging current and the starting charging voltage to obtain a reference harmonic characteristic data set, and performing dynamic harmonic characteristic analysis processing on the dynamic charging current and the dynamic charging voltage to obtain a dynamic harmonic characteristic data set includes:
Carrying out harmonic content calculation processing on the starting charging current and the starting charging voltage through the management system to obtain initial harmonic characteristic data, and extracting main harmonic components of the initial harmonic characteristic data to obtain a reference harmonic characteristic data set;
Performing periodic harmonic measurement processing on the dynamic charging current and the dynamic charging voltage to obtain time sequence harmonic data;
And extracting harmonic parameters in the time sequence harmonic data, and carrying out change trend analysis processing on the harmonic parameters to obtain a dynamic harmonic characteristic data set.
Optionally, in a second implementation manner of the first aspect of the present invention, the extracting a harmonic parameter in the time-series harmonic data, and performing a trend analysis processing on the harmonic parameter, to obtain a dynamic harmonic feature data set includes:
Carrying out sliding window segmentation processing on the time sequence harmonic data to obtain harmonic data subsets of a plurality of time windows, and extracting harmonic features in the harmonic data subsets to obtain a harmonic feature sequence;
Performing adjacent window difference calculation processing on the harmonic characteristic sequence to obtain harmonic parameter change rate data;
Performing stage division processing on the charging operation of the single-channel single-plug charging pile according to the harmonic parameter change rate data to obtain a charging stage identification result;
And carrying out association analysis processing on the charging stage identification result and the corresponding harmonic characteristic sequence to obtain a dynamic harmonic characteristic data set.
Optionally, in a third implementation manner of the first aspect of the present invention, the performing, by the management system, abnormality detection processing on the working state of the single-path single-plug charging pile according to the reference harmonic characteristic data set, the dynamic harmonic characteristic data set and preset normal harmonic data, to obtain a hierarchical abnormality alarm signal includes:
Carrying out dynamic deviation calculation processing on harmonic variation of the charging operation of the single-channel single-plug charging pile according to a dynamic harmonic characteristic data set and preset normal harmonic data by the management system to obtain a harmonic deviation time sequence;
judging whether the charging operation of the single-path single-plug charging pile is abnormal or not according to the harmonic deviation time sequence;
If yes, determining an abnormal type of the working state of the single-way single-plug charging pile according to the harmonic deviation time sequence and the reference harmonic characteristic data set, and generating a grading abnormal alarm signal corresponding to the abnormal type.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the performing, by the management system, dynamic deviation calculation processing on a harmonic variation of a charging operation of the single-path single-plug charging pile according to a dynamic harmonic feature data set and preset normal harmonic data, to obtain a harmonic deviation time sequence includes:
Performing time alignment processing on the dynamic harmonic characteristic data set and preset normal harmonic data, and performing multi-scale deviation calculation on the dynamic harmonic characteristic data set and the preset normal harmonic data which are aligned according to time to obtain a multi-scale harmonic deviation matrix;
carrying out nonlinear feature extraction processing on the multi-scale harmonic deviation matrix to obtain a harmonic abnormal feature vector;
And carrying out dynamic clustering processing on the charging process according to the harmonic abnormal feature vector to obtain a harmonic state evolution sequence, and carrying out time sequence prediction processing on the harmonic state evolution sequence to obtain a harmonic deviation time sequence.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the determining, according to the harmonic deviation time sequence and the reference harmonic characteristic data set, an anomaly type of a working state of the single-path single-plug charging pile, and generating a hierarchical anomaly alarm signal corresponding to the anomaly type includes:
performing time-frequency analysis processing on the harmonic deviation time sequence to obtain a harmonic abnormal characteristic map;
Carrying out matching recognition on the abnormal mode of the single-channel single-plug charging pile according to the harmonic abnormal characteristic map and the reference harmonic characteristic data set to obtain an abnormal type judgment result, wherein the abnormal type judgment result comprises an abnormal type and probability distribution of the abnormal type;
fuzzy reasoning is carried out on the abnormal type judgment result to obtain a comprehensive abnormal evaluation index;
And grading the abnormal state of the single-path single-plug charging pile according to the comprehensive abnormal evaluation index to obtain a grading abnormal alarm signal.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the performing, by the management system, intelligent decision processing on a charging operation of the single-path single-plug charging pile according to a hierarchical anomaly alarm signal, to obtain a charging control instruction, and adjusting the charging operation of the single-path single-plug charging pile according to the charging control instruction includes:
the management system performs multi-mode feature extraction processing on charging process data of the charging operation of the single-channel single-plug charging pile according to the grading abnormal alarm signal to obtain a high-dimensional abnormal feature tensor;
Performing topology mapping processing on the charge state space according to the high-dimensional abnormal characteristic tensor to obtain an abnormal state topological graph, and performing graph neural network analysis processing on the abnormal state topological graph to obtain an abnormal propagation prediction model;
Performing meta heuristic algorithm solving processing on the multi-objective charging optimization problem according to the abnormal propagation prediction model to obtain a pareto optimal solution set, and performing fuzzy decision processing on the pareto optimal solution set to obtain a fuzzy charging control strategy;
And carrying out self-adaptive model prediction control processing on the charging operation of the single-channel single-plug charging pile according to the fuzzy charging control strategy to obtain a charging control instruction, and adjusting the charging operation of the single-channel single-plug charging pile according to the charging control instruction.
The second aspect of the present invention provides a charging detection device of a charger, the charger includes a single-way single-plug charging pile, a harmonic electric energy meter, and a management system, the charging detection device of the charger includes:
The data detection module is used for acquiring the starting charging current and the starting charging voltage, the dynamic charging current and the dynamic charging voltage of the single-channel single-plug charging pile through the harmonic electric energy meter when the charging operation of the single-channel single-plug charging pile is started and the charging operation is performed;
The harmonic analysis module is used for carrying out initial harmonic characteristic analysis processing on the starting charging current and the starting charging voltage through the management system to obtain a reference harmonic characteristic data set, and carrying out dynamic harmonic characteristic analysis processing on the dynamic charging current and the dynamic charging voltage to obtain a dynamic harmonic characteristic data set;
the abnormality detection module is used for carrying out abnormality detection processing on the working state of the single-channel single-plug charging pile through the management system according to the reference harmonic characteristic data set, the dynamic harmonic characteristic data set and preset normal harmonic data to obtain a hierarchical abnormality alarm signal;
The intelligent decision module is used for performing intelligent decision processing on the charging operation of the single-way single-plug charging pile according to the grading abnormal alarm signal through the management system to obtain a charging control instruction, and adjusting the charging operation of the single-way single-plug charging pile according to the charging control instruction.
The third aspect of the invention provides a charging detection device of a charger, which comprises a memory and at least one processor, wherein the memory is stored with instructions, the memory and the at least one processor are interconnected through a line, and the at least one processor calls the instructions in the memory so that a charging detection device of the charger executes the steps of the charging detection method of the charger.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein that, when run on a computer, cause the computer to perform the steps of the charger charge detection method described above.
According to the charging detection method, the charging detection device, the charging detection equipment and the storage medium, starting and dynamic charging current and voltage data are obtained through the harmonic electric energy meter in the starting and charging process of the charging pile. The management system performs initial harmonic characteristic analysis on the starting current and the starting voltage to generate a reference harmonic characteristic data set, and performs dynamic harmonic characteristic analysis on the dynamic current and the dynamic voltage to generate a dynamic harmonic characteristic data set. And carrying out abnormality detection on the working state of the charging pile based on the reference, the dynamic harmonic data and the preset normal harmonic data, and generating a grading abnormality alarm signal. And making an intelligent decision according to the alarm signal, sending a charging control instruction, and dynamically adjusting the charging operation of the charging pile. According to the invention, through analyzing the harmonic characteristics of the charging current and the voltage, the abnormal state in the charging process is accurately detected, the abnormal detection precision is improved, the adjustment is made according to different abnormal conditions, and the safety and the reliability of the charging operation are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a schematic diagram of a first embodiment of a charging detection method of a charger according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of a charging detection device of a charger according to an embodiment of the present invention;
Fig. 3 is a schematic diagram of an embodiment of a charging detection apparatus of a charger in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "comprising" and "having" and any variations thereof, as used in the embodiments of the present invention, are intended to cover non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed or inherent to such process, method, article, or apparatus but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
For the convenience of understanding the present embodiment, a detailed description will be given of a charging detection method of a charger disclosed in the embodiment of the present invention. As shown in fig. 1, the method comprises the following steps:
101. When the charging operation of the single-channel single-plug charging pile is started and the charging operation is performed, acquiring starting charging current and starting charging voltage, dynamic charging current and dynamic charging voltage of the single-channel single-plug charging pile through the harmonic electric energy meter;
In one embodiment of the present invention, it is first necessary to install an electric energy meter with a harmonic measurement function on a single-way single-plug charging pile. Such an electric energy meter should be capable of measuring the harmonic content of voltages and currents in real time, including Total Harmonic Distortion (THD) and the amplitude of each subharmonic. According to the research results, the 3, 5, 7 harmonics are of great importance, as these harmonics contribute the most during the charging of electric vehicles. The sampling frequency of the electric energy meter is set to be not lower than 10 kHz so as to ensure that the harmonic wave within 50 times can be accurately captured. Meanwhile, the electric energy meter is provided with a communication function, and can transmit measurement data to the charging pile management system in real time. When the electric automobile is inserted into the charging pile and starts to charge, the harmonic electric energy meter immediately starts a measuring program. During the first 3 minutes of the start of charging, the system performs an initial harmonic signature analysis, and this time period is selected based on the observation that the charging power reaches a steady state about 3 minutes after the start of charging. At this initial stage, the system records the THD values of the voltage and current, the magnitudes of the 3, 5, 7 harmonics (percentage relative to the fundamental) and the charging power curve. These initial data constitute harmonic features of the starting charge current and the starting charge voltage. In the whole charging process, the harmonic electric energy meter continuously monitors the harmonic content of charging current and voltage. The system records complete harmonic data, including THD and amplitude of at least 25 harmonics, every 1 minute. The selection of this sampling interval is based on practical experience and can capture harmonic variation trends while not generating excessive data. For each sampling point, the system calculates the THD values of the voltage and current, the amplitude of the main harmonic (3, 5, 7), the amplitude of the even harmonic (especially 2, 4), and the dc component. These data constitute harmonic features of the dynamic charge current and the dynamic charge voltage. To improve the accuracy and reliability of the measurements, the system processes harmonic data using a sliding window technique. Each sliding window contains 10 seconds of data with 50% overlap between windows. The method can smooth short-term fluctuation and retain trend information of harmonic variation. A Fast Fourier Transform (FFT) is performed on the data within each sliding window to obtain a more accurate harmonic spectrum. The system also allows for different phases in the charging process to exhibit different harmonic characteristics. Accordingly, the parameters of the harmonic measurement are dynamically adjusted according to the change in the charging power. For example, at the end of charge (when the battery charge exceeds 80%), the system increases the sampling frequency to capture the more rapid harmonic changes that may occur at this stage. To cope with possible measurement errors and outliers, the system implements a data validation mechanism. After each measurement, the results are compared with the average of the previous several measurements. If a significant deviation is found, the system immediately makes additional measurements to confirm the reliability of the data. Meanwhile, the system continuously monitors the working state of the electric energy meter, and ensures that the electric energy meter is always in a normal working state. Finally, all measured harmonic data, including the starting phase and the dynamic data of the whole charging process, are transmitted to the charging pile management system in real time through a safe communication protocol. The management system associates the data with the time stamp to form a complete charging harmonic characteristic time sequence, and provides basic data support for subsequent anomaly detection and intelligent decision.
102. The management system performs initial harmonic characteristic analysis processing on the starting charging current and the starting charging voltage to obtain a reference harmonic characteristic data set, and performs dynamic harmonic characteristic analysis processing on the dynamic charging current and the dynamic charging voltage to obtain a dynamic harmonic characteristic data set;
In one embodiment of the invention, the method for obtaining the dynamic harmonic characteristic data set comprises the steps of carrying out harmonic content calculation processing on the starting charging current and the starting charging voltage through the management system to obtain initial harmonic characteristic data, extracting main harmonic components of the initial harmonic characteristic data to obtain a reference harmonic characteristic data set, carrying out periodic harmonic measurement processing on the dynamic charging current and the dynamic charging voltage to obtain time sequence harmonic data, extracting harmonic parameters in the time sequence harmonic data, and carrying out change trend analysis processing on the harmonic parameters to obtain the dynamic harmonic characteristic data set.
Specifically, the harmonic content calculation processing is performed on the starting charging current and the starting charging voltage through the management system. The process starts from the moment the electric automobile is connected with the charging pile and starts charging, and the system immediately starts data acquisition. The acquired current and voltage waveforms are analyzed by a Fast Fourier Transform (FFT) algorithm, and the Total Harmonic Distortion (THD) and the amplitude of each subharmonic are calculated. During the calculation, the system is particularly concerned with the 3, 5, 7 harmonics, as these typically contribute the most during the charging of an electric vehicle. At the same time, the system also records the amplitude of even harmonics (especially 2, 4) and the dc component, which are critical for a comprehensive evaluation of the harmonic characteristics of the charging process. After the calculation is completed, the system further processes the obtained initial harmonic characteristic data and extracts main harmonic components. The method comprises the steps of firstly sequencing the amplitude values of all subharmonics, and selecting the first few harmonics with the largest amplitude values as main harmonic components. Typically, these major harmonics include the fundamental (50 Hz or 60 Hz) and the 3,5, 7 harmonics. The system also calculates the percentage of these main harmonics relative to the fundamental so as to more intuitively reflect the relative intensities of the harmonics. In addition, the system also records THD values as an indicator of the overall harmonic level. These extracted main harmonic components and THD values together form a baseline harmonic feature dataset that provides a reference baseline for subsequent dynamic analysis. Next, the system performs periodic harmonic measurement processing on the dynamic charge current and the dynamic charge voltage. This process continues throughout the charging cycle, with the system taking a complete harmonic measurement at regular intervals (e.g., every minute). Each measurement adopts the same FFT algorithm as the initial harmonic analysis, and the THD and the amplitude of each subharmonic are calculated. Such periodic measurements ensure that the system is able to capture dynamic changes in harmonic characteristics during charging. To improve the reliability and continuity of the data, the system employs a sliding window technique, each measurement window containing 10 seconds of data with 50% overlap between adjacent windows. The method can smooth short-term fluctuation and retain trend information of harmonic variation. On the basis of periodic measurement, the system extracts key harmonic parameters in the time sequence harmonic data and analyzes and processes the change trend of the parameters. The extracted parameters include THD, amplitude of the main harmonic (3, 5, 7), amplitude of the even harmonic, and dc component. The system calculates the rate of change of these parameters between successive measurement cycles, identifying significant trends and abnormal fluctuations. Meanwhile, the system also analyzes the correlation between harmonic parameters, such as whether the proportional relation between different subharmonic amplitudes is stable. The analysis results together form a dynamic harmonic characteristic data set, and the dynamic change condition of the harmonic characteristic in the charging process is comprehensively reflected. In the whole process, the system continuously monitors the working state of the electric energy meter, and ensures the accuracy of measured data. If an outlier is found in a measurement, the system immediately makes additional measurements to verify the reliability of the data. At the same time, the system also considers that different phases in the charging process may exhibit different harmonic characteristics, and therefore increases the sampling frequency at the end of the charge (e.g., when the battery charge exceeds 80%) to capture the more rapid harmonic changes that may occur at this phase. Finally, the system transmits all measured and analyzed data, including the reference harmonic characteristic data set and the dynamic harmonic characteristic data set, to the central management system in real time through a secure communication protocol. These data are associated with time stamps to form a complete charge harmonic feature time series that provides comprehensive and detailed data support for subsequent anomaly detection and intelligent decision-making. The comprehensive harmonic analysis method not only can accurately capture the harmonic change in the charging process, but also can provide important reference information for the safe operation of the charging pile and the stability of the power grid.
Further, the method for extracting the harmonic parameters in the time sequence harmonic data and carrying out change trend analysis processing on the harmonic parameters to obtain a dynamic harmonic characteristic data set comprises the steps of carrying out sliding window segmentation processing on the time sequence harmonic data to obtain harmonic data subsets of a plurality of time windows, extracting harmonic characteristics in the harmonic data subsets to obtain a harmonic characteristic sequence, carrying out adjacent window difference calculation processing on the harmonic characteristic sequence to obtain harmonic parameter change rate data, carrying out stage division processing on charging operation of the single-channel single-plug charging pile according to the harmonic parameter change rate data to obtain a charging stage identification result, and carrying out association analysis processing on the charging stage identification result and the corresponding harmonic characteristic sequence to obtain the dynamic harmonic characteristic data set.
Specifically, first, a sliding window division process is performed on time-series harmonic data. This step employs a fixed size time window, e.g., 10 seconds, that is slid sequentially along the time series. The step size for each sliding is set to 50% of the window size, i.e. 5 seconds, so that there is a 50% overlap between adjacent windows. This sliding window technique can smooth short term fluctuations while preserving continuity information of harmonic variations. By this processing, subsets of harmonic data for a plurality of time windows are obtained, each subset containing 10 seconds of harmonic data. Next, harmonic features are extracted from each harmonic data subset. The extracted features include Total Harmonic Distortion (THD), the amplitude of the main harmonic (especially 3, 5, 7 harmonics), the amplitude of even harmonics (e.g., 2, 4 harmonics), and the dc component. For each time window, the mean and standard deviation of these features are calculated. In addition, the percentage of each subharmonic amplitude relative to the fundamental is calculated to more intuitively reflect the relative intensities of the harmonics. These extracted features constitute a sequence of harmonic features, one set of feature values for each time window. Then, adjacent window difference calculation processing is carried out on the harmonic characteristic sequence. This step calculates the amount of change in each harmonic characteristic between two adjacent time windows. Specifically, for each feature (e.g., THD, amplitude of each subharmonic, etc.), the difference between the current window and the previous window is calculated and divided by the time interval to obtain the rate of change. The processing obtains the harmonic parameter change rate data, and reflects the dynamic change speed and direction of the harmonic characteristic in the charging process. Based on the obtained harmonic parameter change rate data, the system carries out stage division processing on the charging operation of the single-path single-plug charging pile. This step identifies the different phases in the charging process by analyzing the pattern of the rate of change data. For example, the initial charge may exhibit rapid changes in harmonic parameters, the mid-term may be relatively stable, and the later-term (e.g., when the battery charge exceeds 80%) may again exhibit large fluctuations. The system uses a clustering algorithm (such as K-means or hierarchical clustering) to analyze the rate of change data and divide the overall charging process into several distinct phases, such as a start phase, a steady phase and an end phase. Each phase is assigned a unique identifier, forming a charging phase identification result. And finally, carrying out association analysis processing on the charging stage identification result and the corresponding harmonic characteristic sequence by the system. This step matches and analyzes each charging phase with its corresponding harmonic signature. Specifically, the system calculates statistical indicators, such as mean, standard deviation, maximum and minimum values, for each harmonic characteristic within each charging phase. Meanwhile, the change trend of each harmonic parameter between different charging stages is analyzed, such as whether certain harmonic waves show obvious increasing or decreasing trend in a specific stage. In addition, the system also analyzes the correlation between harmonic parameters, such as whether the proportional relationship between the different subharmonic magnitudes remains stable during each charging phase. The results of this correlation analysis constitute the final dynamic harmonic feature data set. The dynamic harmonic characteristic data set comprehensively reflects the dynamic change condition of the harmonic characteristic in the charging process, and the dynamic harmonic characteristic data set comprises typical harmonic characteristics and change modes of each charging stage. It includes not only specific harmonic parameter values at various time points, but also the trend of these parameters over time and the characteristic behavior at different charging phases.
103. Performing abnormality detection processing on the working state of the single-path single-plug charging pile by the management system according to the reference harmonic characteristic data set, the dynamic harmonic characteristic data set and preset normal harmonic data to obtain a grading abnormality alarm signal;
In one embodiment of the invention, the step of performing abnormality detection processing on the working state of the single-channel single-plug charging pile through the management system according to the reference harmonic characteristic data set, the dynamic harmonic characteristic data set and the preset normal harmonic data to obtain a hierarchical abnormality alarm signal comprises the steps of performing dynamic deviation calculation processing on harmonic changes of charging operation of the single-channel single-plug charging pile through the management system according to the dynamic harmonic characteristic data set and the preset normal harmonic data to obtain a harmonic deviation time sequence, judging whether the charging operation of the single-channel single-plug charging pile is abnormal according to the harmonic deviation time sequence, if yes, determining an abnormality type of the working state of the single-channel single-plug charging pile according to the harmonic deviation time sequence and the reference harmonic characteristic data set, and generating the hierarchical abnormality alarm signal corresponding to the abnormality type.
Specifically, firstly, a management system is used for carrying out dynamic deviation calculation processing on harmonic variation of charging operation of a single-channel single-plug charging pile according to a dynamic harmonic characteristic data set and preset normal harmonic data, and a harmonic deviation time sequence is obtained. This step involves comparing the actual measured harmonic data with a preset normal range, and calculating a deviation value for each time point. And then, the system judges whether the charging operation of the single-way single-plug charging pile is abnormal or not according to the harmonic deviation time sequence. This determination involves a number of steps and techniques. Firstly, the system performs statistical analysis on the harmonic deviation time sequence, and calculates the average deviation value and standard deviation of each harmonic parameter (such as total harmonic distortion THD, main harmonic amplitude, etc.). These statistical indicators provide a basis for subsequent anomaly determination. The system then employs a multi-threshold decision method to identify potential anomalies. For each harmonic parameter, multiple threshold levels are set, such as slight, medium, and severe deviations. The thresholds are set based on preset normal harmonic data and historical operating experience. The system compares the actual deviation value to these thresholds and if the deviation of a certain parameter continues to exceed a certain threshold, it is marked as a potential anomaly. In order to improve the accuracy of anomaly detection, the system also considers time factors. Transient deviations may be caused by transient fluctuations and do not necessarily represent a true anomaly. Thus, the system introduces a time window analysis, which only considers an actual anomaly if the deviation persists within a certain time window. The length of this time window can be dynamically adjusted according to different harmonic parameters and degrees of deviation. In addition, the system utilizes trend analysis to identify progressive anomalies. By calculating the rate of change and acceleration of the harmonic deviation, the system is able to capture those anomalies that, although not exceeding the threshold, exhibit a significant upward trend. This approach helps to find potential problems early and to achieve preventive maintenance. The system also employs correlation analysis taking into account possible interactions between different harmonic parameters. For example, anomalies in certain harmonics may result in chain reactions in other harmonics. The system identifies such complex anomaly patterns by analyzing the correlation between the deviations of the different harmonic parameters. In order to handle noise and outliers that may be present in the data, the system employs filtering techniques. Short-term fluctuation and isolated abnormal points are removed through methods such as median filtering or Kalman filtering, so that abnormal detection is more stable and reliable. The system also considers the impact of the charging phase in the determination. Different charging phases (e.g., start-up phase, steady-state phase, end-phase) may have different harmonic characteristics. And the system adopts different judgment standards for different stages according to the previously obtained charging stage identification result so as to adapt to the dynamic characteristics of the charging process. Finally, the system comprehensively considers all the factors, and uses a machine learning algorithm (such as a support vector machine or a decision tree) to make final abnormality judgment. The algorithm is trained, and factors such as deviation, time duration, change trend, correlation and the like of a plurality of harmonic parameters can be comprehensively considered, so that more accurate abnormality judgment can be made.
Further, the management system is used for carrying out dynamic deviation calculation processing on the harmonic variation of the charging operation of the single-channel single-plug charging pile according to the dynamic harmonic characteristic data set and the preset normal harmonic data, and obtaining a harmonic deviation time sequence comprises the steps of carrying out time alignment processing on the dynamic harmonic characteristic data set and the preset normal harmonic data, and carrying out multi-scale deviation calculation on the dynamic harmonic characteristic data set and the preset normal harmonic data after time alignment to obtain a multi-scale harmonic deviation matrix; and carrying out dynamic clustering processing on the charging process according to the harmonic abnormal feature vector to obtain a harmonic state evolution sequence, and carrying out time sequence prediction processing on the harmonic state evolution sequence to obtain a harmonic deviation time sequence.
Specifically, firstly, time alignment processing is required to be performed on the dynamic harmonic characteristic data set and preset normal harmonic data. The time alignment process employs a dynamic time warping (DYNAMIC TIME WARPING, DTW) algorithm that is capable of handling nonlinear time warping that may exist in the time series data so that the two sets of data can be optimally matched on the time axis. And after the time alignment is completed, performing multi-scale deviation calculation on the dynamic harmonic characteristic data set aligned by the system and preset normal harmonic data. This step uses wavelet transform techniques to decompose the harmonic data into components of different time scales. A Discrete Wavelet Transform (DWT) or a Continuous Wavelet Transform (CWT) is typically selected, and an appropriate wavelet basis function, such as Daubechies wavelet or Morlet wavelet, is selected based on the characteristics of the data. For each time scale, calculating the deviation between the actual harmonic data and the preset normal range. This multi-scale analysis can capture harmonic anomalies on different time scales, from fast fluctuations to long-term trends can be identified. The result of the calculation forms a multi-scale harmonic deviation matrix, wherein each row represents a time scale and each column represents a point in time. Next, nonlinear feature extraction processing is performed on the multi-scale harmonic deviation matrix. This step aims at extracting low-dimensional features from the high-dimensional bias matrix that can effectively characterize harmonic anomalies. The processing uses nonlinear dimension reduction techniques such as kernel principal component analysis (KERNEL PCA). The method can keep nonlinear structures in the data and better capture complex harmonic abnormal modes. The result of the feature extraction is a harmonic anomaly feature vector that contains key information that can summarize the state of the harmonic anomaly. And then, the system performs dynamic clustering processing on the charging process according to the harmonic abnormal feature vector. This step uses an online clustering algorithm such as online K-means or stream DBSCAN (density clustering). The algorithm can process the incoming data in real time, and each time point in the charging process is distributed into different clusters according to the harmonic abnormal characteristics. The clustering result reflects the dynamic change of the harmonic state in the charging process, and a harmonic state evolution sequence is formed. This sequence describes how the harmonic state is transferred from one cluster to another during charging, embodying the dynamic evolution of harmonic anomalies. Finally, the system carries out time sequence prediction processing on the harmonic state evolution sequence to obtain a harmonic deviation time sequence. This step employs a time series prediction model, such as long short term memory network (LSTM) or gate loop unit (GRU). These deep learning models are capable of capturing long-term dependencies in time series data, and are suitable for processing complex time series data such as harmonic states. The predictive model predicts a harmonic state change over a period of time in the future based on a historical harmonic state evolution sequence. The predicted result is compared with the actual observed harmonic state to generate a final harmonic deviation time sequence. The harmonic deviation time sequence not only comprises harmonic abnormal information at the current moment, but also comprises prediction of future harmonic changes. The method integrates the results of multi-scale analysis, nonlinear feature extraction, dynamic clustering and time sequence prediction, and provides a comprehensive and detailed harmonic anomaly description. The method can capture complex harmonic abnormal modes including transient abnormal, progressive abnormal, periodic abnormal and the like. Meanwhile, by predicting future harmonic changes, the system can identify potential anomalies in advance, and preventive maintenance and control are realized.
Further, determining the abnormal type of the working state of the single-channel single-plug charging pile according to the harmonic deviation time sequence and the reference harmonic characteristic data set, generating a grading abnormal alarm signal corresponding to the abnormal type comprises the steps of carrying out time-frequency analysis processing on the harmonic deviation time sequence to obtain a harmonic abnormal characteristic map, carrying out matching recognition on the abnormal mode of the single-channel single-plug charging pile according to the harmonic abnormal characteristic map and the reference harmonic characteristic data set to obtain an abnormal type judging result, wherein the abnormal type judging result comprises the abnormal type and probability distribution of the abnormal type, carrying out fuzzy reasoning processing on the abnormal type judging result to obtain a comprehensive abnormal evaluation index, and carrying out grading processing on the abnormal state of the single-channel single-plug charging pile according to the comprehensive abnormal evaluation index to obtain the grading abnormal alarm signal.
Specifically, firstly, time-frequency analysis processing is carried out on the harmonic deviation time sequence to obtain a harmonic abnormal characteristic map. This step converts the time domain signal into a time-frequency domain representation using short-time fourier transform (STFT) or Wavelet Transform (WT) techniques. STFT is suitable for analyzing the local frequency characteristics of the signal, while WT can provide multi-resolution analysis, suitable for processing non-stationary signals. During processing, the system selects the appropriate window function (e.g., hanning window or gaussian window) and window size to balance time and frequency resolution. For each time window, a spectrum is calculated and the results are combined into a time-frequency spectrum. This harmonic anomaly signature intuitively demonstrates the distribution characteristics of harmonic anomalies in both the time and frequency dimensions. And then, the system performs matching recognition on the abnormal mode of the single-way single-plug charging pile according to the harmonic abnormal characteristic map and the reference harmonic characteristic data set to obtain an abnormal type judgment result. This step employs pattern recognition techniques such as Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs). The system firstly uses a standard harmonic characteristic data set training model to establish characteristic templates of different anomaly types. And then, inputting the obtained harmonic abnormal characteristic spectrum into a trained model for classification. The model output contains anomaly types and their probability distributions, reflecting the likelihood of different anomaly types. The probability output considers the uncertainty of the abnormal type judgment in the actual situation, and provides more abundant information for the subsequent processing. And then, the system carries out fuzzy reasoning processing on the abnormal type judgment result to obtain the comprehensive abnormal evaluation index. This step uses a fuzzy logic system to convert the exact probability values into fuzzy sets. The fuzzy inference system comprises three main parts, namely fuzzification, inference rules and defuzzification. In the fuzzification stage, the system maps probability values for anomaly types to predefined fuzzy sets (e.g., "low probability", "medium probability", "high probability"). The inference rules are formulated based on expert knowledge and historical data, such as "if the probability of type a is high and the probability of type B is medium, the degree of comprehensive abnormality is severe". The defuzzification process converts the fuzzy reasoning result into a specific numerical value which is used as a comprehensive abnormal evaluation index. the fuzzy reasoning method can better process uncertainty and ambiguity in abnormal judgment and provide an evaluation result which is more in line with human intuition. And finally, the system carries out grading processing on the abnormal state of the single-path single-plug charging pile according to the comprehensive abnormal evaluation index to obtain a grading abnormal alarm signal. This step uses hierarchical clustering algorithms or rule-based classification methods. The system predefines a number of anomaly levels, such as mild, moderate, severe and critical. Then, the current abnormal state is classified into a corresponding class according to the value of the comprehensive abnormality evaluation index. In the partitioning process, the system considers factors such as severity, duration, and potential impact of the anomaly. For example, even if the current abnormality evaluation index is not high, it may be classified into a higher level if the duration is long or a rapid deterioration trend is exhibited. For each anomaly level, the system generates a corresponding alarm signal containing the anomaly type, severity, possible cause, and suggested action to take. The whole process forms a complete anomaly detection and alarm generation chain. Starting from time-frequency analysis, the system gradually converts complex harmonic data into clear and operable alarm information through pattern recognition and fuzzy reasoning to final grading. The method not only considers the complexity and diversity of harmonic anomalies, but also introduces uncertainty processing and human expert knowledge, so that the anomaly detection result is more reliable and practical. Meanwhile, the hierarchical alarm mechanism can help operation and maintenance personnel to quickly understand the severity and urgency of the abnormality, and corresponding measures can be taken in a targeted manner.
104. And carrying out intelligent decision processing on the charging operation of the single-channel single-plug charging pile according to the grading abnormal alarm signal by the management system to obtain a charging control instruction, and adjusting the charging operation of the single-channel single-plug charging pile according to the charging control instruction.
In one embodiment of the invention, the intelligent decision processing is carried out on the charging operation of the single-channel single-plug charging pile according to the grading abnormal alarm signal by the management system to obtain a charging control instruction, and the charging operation of the single-channel single-plug charging pile is regulated according to the charging control instruction; performing topology mapping processing on a charging state space according to the high-dimensional abnormal characteristic tensor to obtain an abnormal state topological graph, performing graph neural network analysis processing on the abnormal state topological graph to obtain an abnormal propagation prediction model, performing meta heuristic algorithm solving processing on a multi-objective charging optimization problem according to the abnormal propagation prediction model to obtain a pareto optimal solution set, performing fuzzy decision processing on the pareto optimal solution set to obtain a fuzzy charging control strategy, performing self-adaptive model prediction control processing on charging operation of a single-way single-plug charging pile according to the fuzzy charging control strategy to obtain a charging control instruction, and adjusting the charging operation of the single-way single-plug charging pile according to the charging control instruction.
Specifically, firstly, the management system performs multi-mode feature extraction processing on charging process data of the charging operation of the single-path single-plug charging pile according to the hierarchical abnormality alarm signal to obtain a high-dimensional abnormality feature tensor. This step employs deep learning techniques, such as Convolutional Neural Networks (CNNs) and long-term memory networks (LSTMs), to process data in the time, frequency and energy domains, respectively. CNN is used to extract spatial features such as local modes of harmonic spectrum, while LSTM captures long-term dependencies of time series. In addition, the system integrates environmental information such as temperature and humidity data around the charging post. Features of these different modalities are fused into a unified high-dimensional outlier feature tensor by tensor decomposition and reconstruction methods, such as Tucker decomposition or CANDECOMP/PARAFAC (CP) decomposition. The tensor comprehensively characterizes the abnormal state in the charging process and contains comprehensive information of time, frequency and environmental factors. Then, the system performs topology mapping processing on the charging state space according to the high-dimensional abnormal characteristic tensor to obtain an abnormal state topology diagram. This step uses a manifold learning algorithm, such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection). These algorithms can map high-dimensional data into low-dimensional space while preserving local relationships between data points. The mapping results form a visualized topology where each point represents a state of charge and the distance between the points reflects the similarity of the states. This topology intuitively shows the relationship and evolution path between different abnormal states. And then, the system performs graph neural network analysis processing on the abnormal state topological graph to obtain an abnormal propagation prediction model. This step uses either a graph roll-up network (GCN) or a graph annotation network (GAT). These models take as input the topology map and learn the interactions between nodes (abnormal states). Through multi-layer graph convolution or attention mechanisms, the model is able to capture complex nonlinear relationships and long-range dependencies. In the training process, the sequence of abnormal state evolution observed in the historical data is used as a label. After training, the model can predict the probability and degree of other states being affected under a given abnormal state, so as to simulate the abnormal propagation process. And then, the system carries out meta heuristic algorithm solving processing on the multi-objective charging optimization problem according to the abnormal propagation prediction model to obtain the pareto optimal solution set. This step considers a number of objectives such as minimizing charge time, maximizing battery life, minimizing harmonic interference, etc. The system employs a multi-objective optimization algorithm, such as NSGA-II (non-dominant ordered genetic algorithm II) or MOEA/D (decomposition-based multi-objective evolutionary algorithm). These algorithms generate a series of non-dominant solutions, i.e., pareto optimal solution sets, by modeling the evolution process. Each solution represents one possible charging strategy, balancing between different targets. And then, the system carries out fuzzy decision processing on the pareto optimal solution set to obtain a fuzzy charging control strategy. This step uses Fuzzy AHP or Fuzzy TOPSIS methods. Firstly, a fuzzy decision matrix is established according to expert knowledge and historical experience, and the relative importance of different targets is defined. Each of the pareto optimal solutions is then demapped to a fuzzy set, such as "high efficiency", "low risk", etc. And (3) evaluating the comprehensive performance of each solution by the system through fuzzy reasoning rules, and finally selecting the optimal charging control strategy. The fuzzy decision method can process uncertainty and ambiguity in the decision process and obtain a control strategy which is more in line with the actual situation. And finally, the system carries out self-adaptive model prediction control processing on the charging operation of the single-path single-plug charging pile according to the fuzzy charging control strategy to obtain a charging control instruction. This step uses Nonlinear Model Predictive Control (NMPC) techniques. The system first builds a dynamic model of the charging process, including battery state, charging power, and harmonic generation. Then, in each control period, the NMPC algorithm predicts the system behavior in a future period of time based on the current state and the fuzzy charge control strategy, and optimally controls the actions sequence. The optimization process takes into account various constraints, such as maximum charge power, harmonic limits, etc. The system selects the first action in the optimization sequence as the current charging control instruction, and the current charging control instruction comprises specific parameters such as charging current, voltage adjustment and the like. And the system adjusts the charging operation of the single-way single-plug charging pile according to the generated charging control instruction. This includes adjusting the charging current, voltage in real time, and suspending or resuming the charging process if necessary. At the same time, the system continuously monitors feedback of the charging process, including actual charging parameters, battery status, and generated harmonics, etc. These feedback data are used to update the system model and optimize the control strategy to form a closed loop control system.
In the embodiment, the starting and dynamic charging current and voltage data are obtained through the harmonic electric energy meter in the starting and charging process of the charging pile. The management system performs initial harmonic characteristic analysis on the starting current and the starting voltage to generate a reference harmonic characteristic data set, and performs dynamic harmonic characteristic analysis on the dynamic current and the dynamic voltage to generate a dynamic harmonic characteristic data set. And carrying out abnormality detection on the working state of the charging pile based on the reference, the dynamic harmonic data and the preset normal harmonic data, and generating a grading abnormality alarm signal. And making an intelligent decision according to the alarm signal, sending a charging control instruction, and dynamically adjusting the charging operation of the charging pile. According to the invention, through analyzing the harmonic characteristics of the charging current and the voltage, the abnormal state in the charging process is accurately detected, the abnormal detection precision is improved, the adjustment is made according to different abnormal conditions, and the safety and the reliability of the charging operation are improved.
The charging detection method of the charger in the embodiment of the present invention is described above, and the charging detection device of the charger in the embodiment of the present invention is described below, where the charger includes a single-path single-plug charging pile, a harmonic electric energy meter, and a management system, referring to fig. 2, and one embodiment of the charging detection device of the charger in the embodiment of the present invention includes:
The data detection module 201 is configured to obtain, when a charging operation of the single-channel single-plug charging pile is started and the charging operation is performed, a starting charging current and a starting charging voltage, a dynamic charging current and a dynamic charging voltage of the single-channel single-plug charging pile through the harmonic electric energy meter;
The harmonic analysis module 202 is configured to perform initial harmonic characteristic analysis processing on the starting charging current and the starting charging voltage through the management system to obtain a reference harmonic characteristic data set, and perform dynamic harmonic characteristic analysis processing on the dynamic charging current and the dynamic charging voltage to obtain a dynamic harmonic characteristic data set;
the abnormality detection module 203 is configured to perform abnormality detection processing on the working state of the single-path single-plug charging pile according to the reference harmonic characteristic data set, the dynamic harmonic characteristic data set and preset normal harmonic data through the management system, so as to obtain a hierarchical abnormality alarm signal;
The intelligent decision module 204 is configured to perform intelligent decision processing on the charging operation of the single-path single-plug charging pile according to the hierarchical anomaly alarm signal through the management system, obtain a charging control instruction, and adjust the charging operation of the single-path single-plug charging pile according to the charging control instruction.
In the embodiment of the invention, the charging detection device of the charger runs the charging detection method of the charger, and the charging detection device of the charger acquires starting and dynamic charging current and voltage data through the harmonic electric energy meter in the starting and charging processes of the charging pile. The management system performs initial harmonic characteristic analysis on the starting current and the starting voltage to generate a reference harmonic characteristic data set, and performs dynamic harmonic characteristic analysis on the dynamic current and the dynamic voltage to generate a dynamic harmonic characteristic data set. And carrying out abnormality detection on the working state of the charging pile based on the reference, the dynamic harmonic data and the preset normal harmonic data, and generating a grading abnormality alarm signal. And making an intelligent decision according to the alarm signal, sending a charging control instruction, and dynamically adjusting the charging operation of the charging pile. According to the invention, through analyzing the harmonic characteristics of the charging current and the voltage, the abnormal state in the charging process is accurately detected, the abnormal detection precision is improved, the adjustment is made according to different abnormal conditions, and the safety and the reliability of the charging operation are improved.
The charging detection apparatus of the middle charger in the embodiment of the present invention is described in detail from the point of view of the modularized functional entity in fig. 2 above, and the charging detection device of the charger in the embodiment of the present invention is described in detail from the point of view of hardware processing below.
Fig. 3 is a schematic structural diagram of a charging detection device of a charger according to an embodiment of the present invention, where the charging detection device 300 of the charger may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (e.g., one or more processors) and a memory 320, and one or more storage mediums 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. Wherein memory 320 and storage medium 330 may be transitory or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instruction operations in the charge detection device 300 of the charger. Still further, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the charging detection device 300 of the charger to implement the steps of the charging detection method of the charger described above.
The charger's charge detection device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input/output interfaces 360, and/or one or more operating systems 331, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the configuration of the charge detection device of the charger shown in fig. 3 is not limiting of the charge detection device of the charger provided by the present invention, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, in which instructions are stored which, when executed on a computer, cause the computer to perform the steps of the charging detection method of the charger.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the system or apparatus and unit described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit and scope of the embodiments of the invention.
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