A method and system for detecting abnormalities in a DC charging pile

Abstract

The present invention provides a method and system for detecting anomalies in DC charging piles. The method includes: selecting the power indication value of the main meter in the charging station and the power indication value of the charging pile to form meter-pile paired measurement data; constructing an equation for solving the meter-pile measurement error coefficient, and extracting the characteristic parameters representing the measurement performance in the solution equation; quantifying the unknown parameters in the characteristic parameters based on the rectification loss mathematical model, and solving the specific values of the characteristic parameters; taking the mean value of the characteristic parameters as the main meter measurement performance parameter, and judging whether the main meter measurement performance is qualified based on the position of the main meter measurement performance parameter in the normal distribution of the historical test data of the charging pile; extracting the qualified meter-pile paired measurement data, identifying the outliers in the meter-pile paired measurement data through the symmetrically optimized box plot anomaly detection algorithm, and locating the charging pile with abnormal measurement performance. Through this solution, online and real-time detection of charging pile anomalies can be achieved, with high detection efficiency and low implementation cost, and it can avoid deviations in electricity settlement.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging piles, and in particular relates to a method and system for detecting anomalies in a DC charging pile. Background Art

[0002] As a large current power transmission and metering equipment, DC charging piles for electric vehicles are closely related to the personal safety and property safety of users. During long-term operation, DC charging piles are affected by factors such as temperature, humidity, and magnetic field strength, which leads to degradation of metering performance and even metering performance errors, which in turn affects the fairness of power trade. Therefore, it is necessary to test the metering performance of DC charging piles.

[0003] At present, the traditional charging pile measurement verification method uses a load to simulate electric vehicles for charging, connects standard measuring instruments in series into the circuit, and compares the display value of the standard measuring instrument with the display value of the charging pile after charging the set electric energy, so as to determine the display error of the charging pile. However, as residential electrical equipment, DC charging piles have the characteristics of scattered distribution, large quantity, and uneven quality. For measurement performance, the verification regulations stipulate that the charger should be verified in a three-year cycle, which has poor timeliness, low detection efficiency and high implementation cost. Obviously, if the metering performance of the charging pile is abnormal during the two verification periods, the existing method cannot detect the abnormal charging pile in time, which will lead to deviations in the charging energy settlement of electric vehicles, undermine the fairness of electricity trade, and fail to timely discover potential risks such as aging and failure of charging pile components that cause abnormal metering performance, endangering personal and property safety. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a DC charging pile abnormality detection method and system, which are used to solve the problems of poor timeliness, low detection efficiency and high implementation cost of current charger abnormality detection.

[0005] In a first aspect of an embodiment of the present invention, a method for detecting an abnormality of a DC charging pile is provided, comprising:

[0006] Obtain the electric energy metering data of each DC charging pile in the charging station during operation, and select the total meter electric energy indication of the station and the electric energy indication of the running charging pile in the same period to form meter-pile paired metering data;

[0007] Based on the meter-pile paired metering data, a meter-pile metering error coefficient solution equation for the whole charging process loss of the charging pile is constructed, and characteristic parameters characterizing the meter-pile metering performance in the solution equation are extracted;

[0008] Constructing a rectification loss mathematical model, quantifying unknown parameters in characteristic parameters of meter-pile metering performance based on the rectification loss mathematical model, solving specific values ​​of the characteristic parameters, and storing the specific values ​​in a characteristic data set;

[0009] Extract the characteristic parameter data of the meter-pile measurement performance covering all charging piles from the characteristic dataset, take the mean value of the characteristic parameter data as the total meter measurement performance parameter, and judge whether the total meter measurement performance is qualified based on the position of the total meter measurement performance parameter in the normal distribution of the historical test data of the charging pile;

[0010] Extract the meter-pile paired measurement data with the total meter measurement performance determined to be qualified in the characteristic dataset, identify the outliers in the meter-pile paired measurement data through the symmetric optimized box plot anomaly detection algorithm, and locate the charging piles with abnormal measurement performance.

[0011] In the second aspect of the embodiments of the present invention, a DC charging pile anomaly detection system is provided, including:

[0012] A data screening module, configured to obtain the electric energy measurement data when each DC charging pile in the charging station is running, and select the electric energy indication value of the total meter in the station and the electric energy indication value of the running charging pile at the same time period to form meter-pile paired measurement data;

[0013] A parameter extraction module, configured to construct an equation for solving the meter-pile measurement error coefficient of the loss during the whole charging process of the charging pile based on the meter-pile paired measurement data, and extract the characteristic parameters representing the meter-pile measurement performance in the solving equation;

[0014] A parameter quantization module, configured to construct a rectification loss mathematical model, quantify the unknown parameters in the characteristic parameters of the meter-pile measurement performance based on the rectification loss mathematical model, solve the specific numerical values of the characteristic parameters, and store the specific numerical values in the characteristic dataset;

[0015] A total meter detection module, configured to extract the characteristic parameter data of the meter-pile measurement performance covering all charging piles from the characteristic dataset, take the mean value of the characteristic parameter data as the total meter measurement performance parameter, and judge whether the total meter measurement performance is qualified based on the position of the total meter measurement performance parameter in the normal distribution of the historical test data of the charging pile;

[0016] A charging pile detection module, configured to extract the meter-pile paired measurement data with the total meter measurement performance determined to be qualified in the characteristic dataset, identify the outliers in the meter-pile paired measurement data through the symmetric optimized box plot anomaly detection algorithm, and locate the charging piles with abnormal measurement performance.

[0017] In the third aspect of the embodiments of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described in the first aspect of the embodiments of the present invention are implemented.

[0018] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the embodiments of the present invention are implemented.

[0019] In the embodiments of the present invention, by selecting the measurement data of the meter-pile pairing, solving the characteristic parameters representing the meter-pile measurement performance and quantifying the characteristic parameters, and judging whether the total meter measurement performance is qualified based on the mean value of the characteristic parameters of the meter-pile measurement performance and the historical test data distribution, and identifying the outliers in the qualified meter-pile pairing measurement data based on the symmetric optimization box plot anomaly detection algorithm, the abnormal charging pile positioning is realized. Thus, it can not only meet the real-time and accurate abnormal positioning requirements of DC charging piles, but also effectively improve the abnormal detection efficiency of charging piles and reduce the detection cost. Furthermore, it can avoid the deviation in the charging electricity settlement, damage the fairness of the electricity trade, and can timely discover problems such as the aging and failure of the charging pile components that cause abnormal measurement performance, and ensure personal and property safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings according to these drawings without creative efforts.

[0021] Figure 1 It is a schematic flow chart of a method for detecting abnormal conditions of a DC charging pile provided by an embodiment of the present invention;

[0022] Figure 2 It is a schematic structural diagram of a system for detecting abnormal conditions of a DC charging pile provided by an embodiment of the present invention;

[0023] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to make the invention objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0025] It should be understood that the term "including" and other similar expressions in the specification or claims of the present invention and the above-mentioned drawings mean covering non-exclusive inclusion. For example, a process, method, system, or device including a series of steps or units is not limited to the listed steps or units. In addition, "first" and "second" are used to distinguish different objects and are not used to describe a specific order.

[0026] Please refer to Figure 1 , a schematic flowchart of a method for detecting abnormalities in a DC charging pile provided by an embodiment of the present invention, including:

[0027] S101. Obtain the power metering data of each DC charging pile during operation in the charging station, and select the power indication value of the total meter in the station and the power indication value of the operating charging pile during the same period to form meter-pile paired metering data;

[0028] A DC charging pile is a device that is fixedly installed outside an electric vehicle, connected to an AC power grid, and outputs adjustable direct current to charge the in-vehicle battery. In a charging station, the total meter measures the power consumed by all charging piles in the charging station, and a single charging pile is responsible for the power consumed by charging the pile body. Charging stations of different scales will be configured with different numbers of charging piles.

[0029] The power metering data includes the power indication value of the total meter and the power indication value of the charging pile. In the time series, pairing the power indication value of the total meter in the station and the power indication value of the operating charging pile during the same period can obtain meter-pile paired metering data.

[0030] Exemplarily, for a DC charging station with a specific number of charging piles configured as 6, the total indication value for the time period is , the indication value of charging pile No. is . Assuming that within the time period , a total of 3 charging piles are participating in charging, numbered 1, 3, and 5 respectively. Therefore, the power indication values of the total meter and the 3 charging piles at this time ( ) can be stored as meter-pile paired metering data . The meter-pile paired data combinations for different time periods can form a paired time series data set .

[0031] Preferably, pair the power indication value of the total meter in the station and the power indication value of the operating charging pile during the time period when only 1 charging pile is operating to form a meter-pile paired metering data set.

[0032] Extract the meter-pile paired metering data for the time period when only 1 charging pile is participating in charging to form a new meter-pile paired metering data set , as the data source for the subsequent method. In the data set In each set of paired data, there is only the master meter data and the data of one charging pile.

[0033] S102. Based on the meter-pile paired measurement data, construct an equation for solving the meter-pile measurement error coefficient of the entire charging process loss of the charging pile, and extract the characteristic parameters representing the meter-pile measurement performance in the solving equation;

[0034] Among them, the equation for solving the meter-pile measurement error coefficient of the entire charging process loss is expressed as:

[0035] ; (1)

[0036] Based on the known quantities in the solving equation, the characteristic parameters representing the meter-pile measurement performance are expressed as:

[0037] (2)

[0038] In the formula, , are respectively the measurement error coefficients of the master meter and the th charging pile, is the master meter power indication value in the time period , is the th charging pile rectification loss, is the power indication value of the th charging pile in the time period , is the other loss during this charging, is the characteristic parameter used to represent the meter-pile measurement performance.

[0039] It can be understood that when the master meter measurement performance is stable, is much smaller than and is approximately 1, can be regarded as an inherent offset amount A; when the master meter measurement performance is out of tolerance, it will increase the fluctuation of the characteristic parameter of the charging pile measurement performance.

[0040] Since the measurement accuracy level of the master meter is higher than that of the charging pile, and the operating environment of the master meter is better than that of the charging pile, therefore is much smaller than , and is relatively stable. and are both known quantities, is the rectification loss, and its magnitude is related to the charging power of the electric vehicle. is the fixed power consumption loss such as the display of the charging pile during the charging process. Compared with the electric energy consumed during charging, it can be ignored. Therefore, formula (1) can be simplified to formula (2).

[0041] S103. Construct a mathematical model of rectification loss, quantify the unknown parameters in the characteristic parameters of the meter-pile measurement performance based on the mathematical model of rectification loss, solve the specific values of the characteristic parameters, and store the specific values in the characteristic data set;

[0042] Among them, the output power of the charging pile No. within the time period is expressed as: , and the mathematical model of rectification loss is expressed as: ;

[0043] Input the quantified power indication of the charging pile and the rectification loss of the charging pile into formula (2) to solve the specific values of the characteristic parameters;

[0044] The rectification loss is a mathematical model related to the charging power, which can be obtained after the factory test by the supplier, and can be abbreviated as , so the rectification loss can also be expressed as ;

[0045] The characteristic parameters of the meter-pile measurement performance within the time period can be calculated from the total indication value, the power indication value of the charging pile and the time period . The specific values of the characteristic parameters within the time period can be stored in the characteristic data set.

[0046] Among them, represents the output power of the charging pile No. within the time period , is the power indication value of the charging pile No. for the time period , represents the i-th time period, is the rectification loss of the charging pile No. .

[0047] S104. Extract the characteristic parameter data of the meter-pile measurement performance covering all charging piles from the characteristic data set, take the mean value of the characteristic parameter data as the total meter measurement performance parameter, and judge whether the total meter measurement performance is qualified based on the position of the total meter measurement performance parameter in the normal distribution of the historical test data of the charging pile;

[0048] Extract the characteristic parameter data of the meter-pile measurement performance covering all charging piles within a period of time t from the characteristic data set and take the mean value as the total meter measurement performance parameter.

[0049] Exemplarily, taking a DC charging station with a charging pile configuration of 6 as an example, in the time span t, the first set of characteristic parameter data of the meter-pile measurement performance covering all charging piles is extracted, and the total meter measurement performance parameter is calculated according to the following formula :

[0050] ;

[0051] Extract the remaining groups of characteristic parameter data covering all charging piles in the time span t, obtain the total meter measurement performance parameter , and calculate the final total meter measurement performance parameter .

[0052] It should be understood that all charging piles in the same charging station have the same model and the same operating environment. The measurement performance should follow the same distribution and the distribution mean should be 0. At the same time, most charging piles should maintain normal measurement performance within the time span t, and only a few charging piles have abnormal measurement performance. Therefore, through the average calculation of m groups of characteristic parameter data of the meter-pile measurement performance, , that is, the parameter can effectively characterize the total meter measurement performance.

[0053] The historical test data of the charging pile is the historical data of testing the DC charging pile and the total meter, including the test data before the DC charging station is put into use, the fault test data, etc.

[0054] Specifically, the mean value of the characteristic parameter data of the meter-pile measurement performance is taken to obtain the total meter measurement performance parameter;

[0055] According to the historical test data of the charging pile, a numerical distribution of the characteristic parameters is formed , the mean value of the numerical distribution is , and the variance is ;

[0056] According to the principle, when the total meter measurement performance parameter is within the range, it is determined that the total meter measurement performance is qualified. When the total meter measurement performance parameter is within the range, it is determined that the total meter measurement performance is unqualified.

[0057] S105. Extract the meter-pile paired measurement data with qualified total meter measurement performance in the characteristic data set, identify the outliers in the meter-pile paired measurement data through the symmetric optimized box plot anomaly detection algorithm, and locate the charging piles with abnormal measurement performance.

[0058] Optionally, extract the meter-pile paired measurement data with qualified total meter measurement performance in the characteristic data set to form a data set , data set The number of data is X, centered around 0, and the data is symmetrically replicated to form an extended data set , the extended data set , and the number of data in the extended data set is 2X;

[0059] Based on the principle of the box plot, calculate the limit values of normal data;

[0060] ;

[0061] In the formula, for , is the lower quartile, is the upper quartile, is the interquartile range, is the upper limit of normal data, is the lower limit of normal data;

[0062] According to the upper and lower limits of normal data, identify the outliers in the extended data set , and locate the charging piles with abnormal metering performance according to the source of the outliers.

[0063] In this embodiment, by selecting the metering data of the meter-pile pairing, constructing an equation to extract the characteristic parameters representing the meter-pile metering performance, quantifying the characteristic parameters, based on the distribution of the mean of the meter-pile metering performance characteristic parameters in the historical test data, judging the metering performance of the master meter, and identifying outliers through the symmetrically optimized box plot anomaly detection algorithm to locate the charging piles with abnormal metering performance. It can not only realize the online detection and location of abnormal DC charging piles and abnormal master meters, but also has high detection efficiency, low cost, and can meet the real-time detection requirements. Furthermore, it can not only avoid the deviation in charging electricity settlement and ensure the fairness of electricity trade, but also timely discover problems such as aging and failure of charging pile components to ensure personal and property safety.

[0064] It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0065] Figure 2 FIG. is a schematic structural diagram of a DC charging pile anomaly detection system 20 provided by an embodiment of the present invention. The system 20 includes:

[0066] A data screening module 210, configured to obtain the electricity metering data when each DC charging pile in the charging station operates, and select the electricity indication value of the master meter in the station and the electricity indication value of the operating charging pile at the same time period to form meter-pile paired metering data;

[0067] Preferably, the electric energy indication value of the total station meter during the time period when only one charging pile is operating is paired with the electric energy indication value of the operating charging pile to form a table-pile paired measurement data set.

[0068] The parameter extraction module 220 is configured to construct an equation for solving the table-pile measurement error coefficient of the loss during the entire charging process of the charging pile based on the table-pile paired measurement data, and extract the characteristic parameters representing the table-pile measurement performance in the solving equation;

[0069] Among them, the equation for solving the table-pile measurement error coefficient is expressed as:

[0070] ; (1)

[0071] Based on the known quantities in the solving equation, the characteristic parameters representing the table-pile measurement performance are expressed as:

[0072] (2)

[0073] In the formula, 、 are the measurement error coefficients of the total meter and the th charging pile respectively, is the electric energy indication value of the total meter during the time period , is the th charging pile's rectification loss, is the electric energy indication value of the th charging pile during the time period , is the other loss during this charging, which can be ignored compared to the electric energy consumed during charging, is the characteristic parameter used to represent the table-pile measurement performance.

[0074] The parameter quantization module 230 is configured to construct a rectification loss mathematical model, quantify the unknown parameters in the characteristic parameters of the table-pile measurement performance based on the rectification loss mathematical model, solve the specific values of the characteristic parameters, and store the specific values in the characteristic data set;

[0075] Among them, the output power of the th charging pile during the time period is expressed as: , and the rectification loss mathematical model is expressed as: ;

[0076] The quantified electric energy indication value of the charging pile and the rectification loss of the charging pile are input into formula (2) to solve the specific values of the characteristic parameters;

[0077] Among them, represents the time period Internal Output power of the charging pile numbered For the time period of Power indication of the charging pile numbered Indicates the i-th time period, is Rectification loss of the charging pile numbered

[0078] The total meter detection module 240 is used to extract the characteristic parameter data covering the meter-pile measurement performance of all charging piles from the characteristic data set, take the mean value of the characteristic parameter data as the total meter measurement performance parameter, and judge whether the total meter measurement performance is qualified based on the position of the total meter measurement performance parameter in the normal distribution of the historical test data of the charging pile;

[0079] Specifically, the judging whether the total meter measurement performance is qualified based on the position of the total meter measurement performance parameter in the normal distribution of the historical test data of the charging pile includes:

[0080] Taking the mean value of the characteristic parameter data of the meter-pile measurement performance to obtain the total meter measurement performance parameter;

[0081] Forming a numerical distribution of the characteristic parameters according to the historical test data of the charging pile , the numerical distribution The mean value of is , the variance is ;

[0082] According to Principle, when the total meter measurement performance parameter is within the Range, it is determined that the total meter measurement performance is qualified. When the total meter measurement performance parameter is within the Range, it is determined that the total meter measurement performance is unqualified.

[0083] The charging pile detection module 250 is used to extract the meter-pile paired measurement data with qualified total meter measurement performance in the characteristic data set, identify the outliers in the meter-pile paired measurement data through the symmetric optimization box plot anomaly detection algorithm, and locate the charging piles with abnormal measurement performance.

[0084] Optionally, the charging pile detection module 250 includes:

[0085] The symmetric expansion unit is used to extract the meter-pile paired measurement data with qualified total meter measurement performance in the characteristic data set to form a data set , the number of data in the data set is X, and the data is symmetrically replicated with 0 as the center to form an extended data set , the extended data set , the number of data in the extended data set is 2X;

[0086] A limit calculation unit for calculating the limits of normal data based on the box plot principle;

[0087] ;

[0088] In the formula, for , is the lower quartile, is the upper quartile, is the interquartile range, is the upper limit of normal data, is the lower limit of normal data;

[0089] An anomaly recognition unit for identifying outliers in the extended data set according to the upper and lower limits of normal data, and locating the charging piles with abnormal metering performance according to the source of the outliers.

[0090] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0091] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device is used for detecting abnormal metering performance of charging piles. As Figure 3 shown, the electronic device 3 of this embodiment includes: a memory 310, a processor 320, and a system bus 330. The memory 310 includes a runnable program 3101 stored thereon. Those skilled in the art can understand that Figure 3 the structural diagram of the electronic device shown in

[0092] does not limit the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or different component arrangements. Figure 3 The following specifically introduces each component of the electronic device in conjunction with

[0093] The memory 310 can be used to store software programs and modules. The processor 320 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 310. The memory 310 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the electronic device (such as cached data). In addition, the memory 310 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0094] A runnable program 3101 containing a network request method is stored on the memory 310. The runnable program 3101 can be divided into one or more modules / units, which are stored in the memory 310 and executed by the processor 320 to achieve functions such as abnormal detection and location of charging piles. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the runnable program 3101 in the electronic device 3. For example, the runnable program 3101 can be divided into functional modules such as a data screening module, a parameter extraction module, a parameter quantization module, a main meter detection module, and a charging pile detection module.

[0095] The processor 320 is the control center of the electronic device. It connects various parts of the entire electronic device through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 310, and by calling data stored in the memory 310, it executes various functions of the electronic device and processes data, thereby monitoring the overall state of the electronic device. Optionally, the processor 320 may include one or more processing units; preferably, the processor 320 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 320.

[0096] The system bus 330 is used to connect various functional components inside the computer. It can transmit data information, address information, and control information. Its types can be, for example, PCI bus, ISA bus, CAN bus, etc. The instructions of the processor 320 are transmitted to the memory 310 through the bus, and the memory 310 feeds back data to the processor 320. The system bus 330 is responsible for the data and instruction interaction between the processor 320 and the memory 310. Of course, the system bus 330 can also be connected to other devices, such as a network interface, a display device, etc.

[0097] In the embodiment of the present invention, the runnable program executed by the processor 320 included in the electronic device includes:

[0098] Obtain the electric energy metering data when each DC charging pile in the charging station is running, and select the power indication value of the main meter in the station and the power indication value of the running charging piles in the same time period to form table-pile paired metering data;

[0099] Based on the table-pile paired metering data, construct an equation for solving the table-pile metering error coefficient of the whole process loss of the charging pile during charging, and extract the characteristic parameters representing the table-pile metering performance in the solving equation;

[0100] Construct a mathematical model of rectification loss, quantify the unknown parameters in the characteristic parameters of the meter-pile measurement performance based on the mathematical model of rectification loss, solve the specific values of the characteristic parameters, and store the specific values in the characteristic data set;

[0101] Extract the characteristic parameter data of the meter-pile measurement performance covering all charging piles from the characteristic data set, take the mean value of the characteristic parameter data as the main meter measurement performance parameter, and judge whether the main meter measurement performance is qualified based on the position of the main meter measurement performance parameter in the normal distribution of the historical test data of the charging pile;

[0102] Extract the meter-pile paired measurement data with the main meter measurement performance determined to be qualified from the characteristic data set, identify the outliers in the meter-pile paired measurement data through the symmetric optimization box plot anomaly detection algorithm, and locate the charging piles with abnormal measurement performance.

[0103] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0104] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A DC charging pile abnormality detection method, characterized in that: include: Obtain the electric energy metering data of each DC charging pile in the charging station during operation, and select the total meter electric energy indication of the station and the electric energy indication of the running charging pile in the same period to form meter-pile paired metering data; Based on the meter-pile paired metering data, a meter-pile metering error coefficient solution equation for the whole charging process loss of the charging pile is constructed, and characteristic parameters characterizing the meter-pile metering performance in the solution equation are extracted; Among them, the equation for solving the meter-pile measurement error coefficient is expressed as: ；（1） Based on the known quantities in the solution equation, the characteristic parameters characterizing the metering performance of the meter-pile are expressed as: （2） In the formula, , They are respectively the general table, The measurement error coefficient of the charging pile is For time period The total meter electric energy indication value, for The rectification loss of the charging pile is For time period of Charging pile power indication value, is the other loss during this charging, Characteristic parameters used to characterize the meter-pile metering performance; Constructing a rectification loss mathematical model, quantifying unknown parameters in characteristic parameters of meter-pile metering performance based on the rectification loss mathematical model, solving specific values ​​of the characteristic parameters, and storing the specific values ​​in a characteristic data set; Among them, the time period Inside The output power of the charging pile is expressed as: , the mathematical model of rectification loss is expressed as: ; The quantified charging pile electric energy value and the rectification loss of the charging pile Input formula (2) to solve the table-specific values ​​of characteristic parameters of pile measurement performance; In the formula, Indicates time period Inside The output power of the charging pile is For time period of No. Charging pile power indication value, represents the i-th time period, for The rectification loss of the charging pile; Extract characteristic parameter data covering meter-pile metering performance of all charging piles from the characteristic data set, take the mean of the characteristic parameter data as the total meter metering performance parameter, and judge whether the total meter metering performance is qualified based on the position of the total meter metering performance parameter in the normal distribution of the charging pile historical test data; The meter-pile paired metering data whose total metering performance is judged to be qualified in the feature data set are extracted, and the outliers in the meter-pile paired metering data are identified through the symmetrically optimized box plot anomaly detection algorithm, and the charging piles with abnormal metering performance are located.

2. The method according to claim 1, characterized in that The selection of the total meter power indication in the station and the running charging pile power indication in the same period to form the meter-pile pairing metering data includes: The total meter power indication in the station during the time period when only one charging pile is in operation is paired with the power indication of the operating charging pile to form a meter-pile paired metering data set.

3. The method according to claim 1, characterized in that The determining whether the total metering performance is qualified based on the position of the total metering performance parameter in the normal distribution of the charging pile historical test data includes: The characteristic parameter data of meter-pile metering performance are averaged to obtain the total meter metering performance parameter; The numerical distribution of characteristic parameters is formed based on the historical test data of charging piles , numerical distribution The mean value of , the variance is ; according to Principle, when the total meter performance parameter is in range, the total meter measurement performance is judged to be qualified. When the total meter measurement performance parameters are within range, and determine that the total meter measurement performance is unqualified.

4. The method according to claim 1, characterized in that: The method of identifying abnormal values ​​in meter-pile paired metering data by using a symmetrically optimized box plot anomaly detection algorithm and locating charging piles with abnormal metering performance includes: Extract meter-pile paired metering data with qualified total meter metering performance from the feature data set to form a data set , dataset The number of data in the data set is X, and the data is symmetrically replicated with 0 as the center to form an extended data set , expand the dataset , the number of data in the extended data set is 2X; Based on the box plot principle, calculate the limit of normal data; In the formula, for , is the lower quartile, is the upper quartile, is the interquartile range, is the normal data cap, It is the lower limit of normal data; Identify extended data sets based on the upper and lower limits of normal data The abnormal values ​​in the data are detected, and the charging piles with abnormal metering performance are located according to the sources of the abnormal values.

5. A DC charging pile abnormality detection system, characterized in that: include: The data screening module is used to obtain the electric energy metering data of each DC charging pile in the charging station during operation, and select the electric energy indication of the total meter in the station and the electric energy indication of the charging pile in operation during the same period to form the meter-pile paired metering data; A parameter extraction module is used to construct a meter-pile measurement error coefficient solution equation for the whole charging process loss of the charging pile based on the meter-pile pairing measurement data, and extract characteristic parameters characterizing the meter-pile measurement performance in the solution equation; Among them, the equation for solving the meter-pile measurement error coefficient is expressed as: ；（1） Based on the known quantities in the solution equation, the characteristic parameters characterizing the metering performance of the meter-pile are expressed as: （2） In the formula, , They are respectively the general table, The measurement error coefficient of the charging pile is For time period The total meter electric energy indication value, for The rectification loss of the charging pile is For time period of No. Charging pile power indication value, is the other loss during this charging, Characteristic parameters used to characterize the meter-pile metering performance; The parameter quantification module is used to construct a rectification loss mathematical model, quantify the unknown parameters in the characteristic parameters of the metering performance of the table-pile based on the rectification loss mathematical model, and solve the specific values ​​of the characteristic parameters, and store the specific values ​​in the characteristic data set; Among them, the time period Inside The output power of the charging pile is expressed as: , the mathematical model of rectification loss is expressed as: ; The quantified charging pile electric energy value and the rectification loss of the charging pile Input formula (2) to solve the table-specific values ​​of characteristic parameters of pile measurement performance; In the formula, Indicates time period Inside The output power of the charging pile is For time period of No. Charging pile power indication value, represents the i-th time period, for The rectification loss of the charging pile; The total meter detection module is used to extract characteristic parameter data covering the meter-pile metering performance of all charging piles from the characteristic data set, take the mean of the characteristic parameter data as the total meter metering performance parameter, and judge whether the total meter metering performance is qualified based on the position of the total meter metering performance parameter in the normal distribution of the charging pile historical test data; The charging pile detection module is used to extract the meter-pile paired metering data whose total metering performance is judged to be qualified in the feature data set, identify the outliers in the meter-pile paired metering data through the symmetrically optimized box plot anomaly detection algorithm, and locate the charging piles with abnormal metering performance.

6. The system according to claim 5, characterized in that The method of identifying abnormal values ​​in meter-pile paired metering data by using a symmetrically optimized box plot anomaly detection algorithm and locating charging piles with abnormal metering performance includes: Extract meter-pile paired metering data with qualified total meter metering performance from the feature data set to form a data set , dataset The number of data in the data set is X, and the data is symmetrically replicated with 0 as the center to form an extended data set , expand the dataset , the number of data in the extended data set is 2X; Based on the box plot principle, calculate the limit of normal data; In the formula, for , is the lower quartile, is the upper quartile, is the interquartile range, is the normal data cap, It is the lower limit of normal data; Identify extended data sets based on the upper and lower limits of normal data The abnormal values ​​in the data are detected, and the charging piles with abnormal metering performance are located according to the sources of the abnormal values.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the DC charging pile abnormality detection method according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps of a DC charging pile abnormality detection method as described in any one of claims 1 to 4 are implemented.

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