Electric vehicle charging identification method, device and equipment and medium
By collecting power supply circuit signals to calculate power factor and harmonic characteristics, and dynamically adjusting thresholds to identify electric bicycle charging behavior, the problem of misjudgment in electric bicycle charging identification in existing technologies is solved, achieving high-accuracy and low-cost safety monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies struggle to accurately identify electric bicycle charging behavior, leading to misjudgments and safety hazards, especially in complex electrical environments where accuracy is insufficient.
By collecting AC voltage and current signals in the power supply circuit, the real-time power factor is calculated, and frequency domain signals are obtained by combining fast Fourier transform. The target harmonic content rate and total harmonic distortion rate are screened, and the threshold is dynamically adjusted to identify the charging behavior of electric bicycles. The duration threshold is combined to determine whether to issue an alarm or perform a power-off operation.
It achieves high accuracy in identifying electric bicycle charging in complex power environments, avoiding misjudgments and ensuring the safety of the community and power grid. At the same time, it does not require intrusion into users' homes, reducing deployment costs and transformation difficulties.
Smart Images
Figure CN121784362A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electric vehicle charging identification technology, specifically relating to an electric vehicle charging identification method, device, equipment, and medium. Background Technology
[0002] With the increasing popularity of electric bicycles, the management and safety issues related to their charging have become increasingly prominent. Improper charging practices, such as illegally connected electrical wires, pose a significant fire hazard and a serious threat to community and power grid safety. Therefore, accurate and non-intrusive identification and monitoring of electric bicycle charging behavior is of paramount practical importance.
[0003] Currently, the identification of electrical equipment is mainly based on load characteristic analysis, which involves analyzing the steady-state characteristics of the equipment such as current, voltage, and power. However, many low-power devices have similar steady-state characteristics, resulting in low distinguishability and a high risk of misjudgment. Summary of the Invention
[0004] The purpose of this invention is to provide a method, device, equipment, and medium for identifying electric vehicle charging, thereby solving the problem of inaccurate identification of electric vehicle charging in the prior art.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for identifying electric vehicle charging, comprising: Real-time acquisition of AC voltage and current signals in the power supply circuit; Calculate the real-time power factor based on the AC voltage and current signals; The real-time power factor is compared with the first threshold. If the real-time power factor is lower than the first threshold and gradually increases within a preset time, a fast Fourier transform is performed on the total current signal to obtain the frequency domain signal. The content rate of the target harmonic and the total harmonic distortion rate are then screened based on the frequency domain signal. Otherwise, the signal is reacquired. The content rate and total harmonic distortion rate of the target harmonic are compared with the second threshold. If both the content rate and the total harmonic distortion rate of the target harmonic are higher than the second threshold, the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic is calculated based on the content rate and the total harmonic distortion rate of the target harmonic and compared with the third threshold. Otherwise, the signal is reacquired. If the peak-to-peak current fluctuation exceeds the third threshold, it is determined that there is electric bicycle charging behavior in the line. If the duration of this behavior is less than the preset time threshold, it is ignored; if the duration is greater than the time threshold, an alarm is issued or a power-off operation is performed.
[0006] Preferably, in the step of real-time acquisition of AC voltage and current signals in the power supply circuit, a window length of 1-2 power frequency cycles is used.
[0007] Preferably, the step of calculating the real-time power factor based on the AC voltage signal and the current signal includes: based on t Instantaneous active power is calculated from the voltage and current signals at a given time. The effective value of voltage is calculated based on the sampled voltage value, and the effective value of current is calculated based on the sampled current value; The average power within the sampling window is collected, and the real-time power factor is obtained by comparing the average power within the sampling window with the effective values of voltage and current.
[0008] Preferably, the first threshold and the second threshold are dynamic thresholds that are automatically adjusted based on the load level or peak value.
[0009] Preferably, the step of performing a fast Fourier transform on the total current signal to obtain a frequency domain signal, and then filtering the content of target harmonics and total harmonic distortion rate based on the frequency domain signal includes: The total current signal is subjected to a fast Fourier transform to obtain the frequency domain signal; Obtain the amplitude of the frequency domain signal, and calculate the harmonic frequencies based on the amplitude of the frequency domain signal. : ; in, n For harmonic order; f 1 represents the fundamental frequency, taken as 50. Hz ; Based on the fundamental frequency, calculate n Amplitude of the subharmonic component : ; based on n Amplitude of the subharmonic component I n and fundamental amplitude I 1. Calculate the target harmonic content; Calculate the total harmonic distortion (THD) based on the amplitude of each harmonic: ; In the formula, N 1 represents the maximum harmonic order considered. This represents the current amplitude of the fundamental frequency.
[0010] Preferably, the calculation of the peak-to-peak current fluctuation rate of the target harmonic's current waveform pulsation based on the target harmonic's content rate and total harmonic distortion rate includes: Calculate the peak-to-peak value of the current waveform ripple of the target harmonic.I pp : ; in, I max This represents the maximum value of the current waveform. I min This represents the minimum value of the current waveform; Calculate the average current: ; In the formula, N 1 represents the maximum harmonic order considered. For the first The current value of the subharmonic; Calculate the peak-to-peak current fluctuation rate (PPV): .
[0011] Preferably, the method further includes: establishing a sample library containing typical power factor ranges and harmonic characteristic spectrum templates for different models of electric bicycle chargers, and matching the features calculated in real time with the templates in the sample library.
[0012] In a second aspect, an electric vehicle charging identification device includes: The acquisition module is used to acquire AC voltage and current signals in the power supply circuit in real time; The calculation module is used to calculate the real-time power factor and power factor fluctuation rate based on the AC voltage signal and current signal. The first comparison module is used to compare the real-time power factor with a first threshold. If the real-time power factor is lower than the first threshold and the real-time power factor gradually increases within a preset time, a fast Fourier transform is performed on the total current signal to obtain the frequency domain signal, and the content rate of the target harmonic and the total harmonic distortion rate are screened based on the frequency domain signal; otherwise, the signal is reacquired. The second comparison block is used to compare the content rate and total harmonic distortion rate of the target harmonic with a second threshold. If both the content rate and the total harmonic distortion rate of the target harmonic are higher than the second threshold, the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic is calculated based on the content rate and the total harmonic distortion rate of the target harmonic and compared with a third threshold; otherwise, the signal is reacquired. The identification module is used to determine that there is electric bicycle charging behavior in the line if the peak-to-peak current fluctuation is greater than the third threshold. If the duration of the behavior is less than the preset time threshold, it is ignored; if the duration is greater than the time threshold, an alarm is issued or a power-off operation is performed.
[0013] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the electric vehicle charging identification method described above.
[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the electric vehicle charging identification method.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By using a first threshold, a second threshold, and a preset time threshold, a three-level progressive recognition logic is formed to improve the compatibility of different charger models and ensure high recognition accuracy even in complex power environments. Identification is achieved by collecting and analyzing the total signal of the power supply circuit, which eliminates the need to enter the user's room or install cameras, completely avoiding privacy issues and making it more acceptable to users. At the same time, it can integrate signal collection and processing functions with existing smart meters and leakage current protectors, eliminating the need for additional dedicated equipment and reducing deployment costs and transformation difficulty. By adopting dynamic thresholds, the first and second thresholds can be automatically adjusted according to the total current of the line, adapting to different power consumption scenarios such as low load and high load, and solving the problem of insufficient reliability of fixed thresholds when the load fluctuates greatly. By determining the duration threshold, interference signals that are briefly accessed are filtered out to avoid false alarms or power outages. For irregular charging behavior that continuously exceeds the threshold, alarms or power outage commands can be quickly triggered to achieve early warning and in-process intervention, ensuring the safety of the community and the power grid. Attached Figure Description
[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of an electric vehicle charging identification method according to Embodiment 1 of the present invention; Figure 2 This is a structural block diagram of the electric vehicle charging identification device according to Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation
[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.
[0018] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.
[0019] Example 1 like Figure 1 As shown, an electric vehicle charging identification method is provided, including: S1. Real-time acquisition of AC voltage and current signals in the power supply circuit; Specifically, the power supply circuit operates at a grid frequency of 50Hz. A voltage / current sensor is used as the data acquisition element, with a sampling frequency of 5kHz and a sampling window length of 20ms, corresponding to one power frequency cycle. The sensor acquires the AC voltage signal in the circuit in real time. v ( t ) and current signal i ( t The voltage signal range is 0-220V, and the current signal range is 0-10A. The acquired data is transmitted to the processing unit in digital signal form without additional filtering.
[0020] Preferably, a sampling frequency of 8kHz and a sampling window length of 40ms are used, corresponding to two power frequency cycles, to ensure the integrity of signal acquisition. The acquisition element is a voltage / current sensor with an anti-electromagnetic interference shielding layer. Before acquisition, the signal is subjected to first-order RC low-pass filtering with a cutoff frequency of 1kHz to filter out high-frequency noise. At the same time, the processing unit removes outliers from the acquired sampling data to further improve signal purity and better adapt to subsequent FFT frequency domain analysis. It can resist power grid noise interference, improve the recognition stability in complex power environments, and overcome the shortcomings of existing high-frequency ripple recognition being susceptible to interference and image recognition being limited by the environment.
[0021] S2. Calculate the real-time power factor and power factor fluctuation rate based on the AC voltage signal and current signal; Specifically, it includes the following steps: based on t Instantaneous active power is calculated from voltage and current signals at time t. P ( t ); ; in, v ( t )for t Voltage signal at time, i ( t )for tThe current signal at a given moment; Calculate the effective voltage value based on the sampled voltage value. V r Calculate the effective value of the current based on the sampled current value. I r ; ; ; In the formula, N The total number of sampling points. k The sampling point number, v k The voltage value at the sampling point. i k The sampling point current value; The average power within the sampling window is collected, and the real-time power factor PF is obtained by comparing the average power within the sampling window with the RMS values of voltage and current. ; in, The average power within the sampling window; Subtracting the real-time power factor from two adjacent time points yields the power factor volatility. : ; In the formula, PT[ t [Indicates time] t The real-time power factor, PT[ t -1] indicates time. t Real-time power factor of -1; Using a moving average: ; Among them, PF avg The power factor after moving average. k For the summation index, M This is the length of the sliding window. Specific implementation examples: Set the total number of sampling points N =1000, corresponding to a 5kHz sampling frequency and a 20ms sampling window; Voltage values at each sampling point within a certain sampling window v k The sum of squares is 2.42 × 10. 6 V², the effective value of the voltage is calculated: V r =49.2V; Current values at each sampling point i k The sum of squares is 1.0 × 10 5A², the effective value of the current is calculated: I r =10A; Instantaneous active power within this window P ( t The sum of ) is 2.36 × 10 4 W, average power P avg =23.6W; The real-time power factor PF = 23.6 / (49.2×10) ≈ 0.048; Take time t PF[ t ] = 0.048, time t -1 of PF[ t -1]=0.045, power factor fluctuation rate ΔPF=0.003; With a sliding window length M=5, the power factor after moving average is: PF avg =(0.048+0.045+0.046+0.047+0.049) / 5≈0.047.
[0023] S3. Compare the real-time power factor with the first threshold. If the real-time power factor is lower than the first threshold and the real-time power factor gradually increases within a preset time, perform a fast Fourier transform on the total current signal to obtain the frequency domain signal, and filter the content rate of the target harmonics and the total harmonic distortion rate based on the frequency domain signal; otherwise, return to re-acquire the signal. Specifically, the first threshold is set to 0.6, and the preset time is 10 seconds; the real-time power factor after moving average. P avg The initial value was 0.52, which was lower than the first threshold of 0.6. Subsequently, within 8 seconds, it changed sequentially to 0.53, 0.55, 0.56, and 0.58, showing a gradually increasing trend, thus satisfying the PF (Power Factor) condition. avg If the power factor fluctuation rate ΔPF is below the first threshold and gradually increases within a preset time, i.e., the power factor fluctuation rate ΔPF is positive, then proceed to step S4. If at a certain moment P avg If the value is higher than the first threshold, or if the power factor fluctuation rate ΔPF is negative, return to step S1 to reacquire the signal.
[0024] Perform a Fast Fourier Transform (FFT) on the total current signal to obtain the frequency domain signal, and then filter the content of target harmonics and the total harmonic distortion rate based on the frequency domain signal. Specifically, this includes the following steps: The total current signal is subjected to a Fast Fourier Transform (FFT) to obtain the frequency domain signal. Based on the frequency domain signal, the content of target harmonics and the total harmonic distortion rate are screened, including: For total current signal i ( t Perform a Fast Fourier Transform to obtain the frequency domain signal. : ; The result of Fourier transform I ( f () is in complex form and contains dimensional and phase information: Obtain the amplitude of the frequency domain signal : ; In the formula, For the real part, It is the imaginary part; Calculate harmonic frequencies based on the amplitude of the frequency domain signal. : ; in, n For harmonic order; f 1 represents the fundamental frequency, taken as 50. Hz ; Based on the fundamental frequency, calculate n Amplitude of the subharmonic component For example, 3 times, 5 times, 7 times: ; ; based on n Amplitude of the subharmonic component I n and fundamental amplitude I 1. Calculate the target harmonic content: ( I n / I 1) × 100%; Calculate the total harmonic distortion (THD) based on the amplitude of each harmonic: ; In the formula, N 1 represents the maximum harmonic order considered. This represents the current amplitude of the fundamental frequency.
[0025] Preferably, the FFT transform uses the Hanning window function to suppress spectral leakage and improve the accuracy of harmonic amplitude calculation; The target harmonics employ an adaptive filtering strategy: Automatically identify the top 3 highest harmonics in terms of amplitude, and prioritize matching the 2nd, 3rd, 5th and 7th harmonics as target harmonics, without needing to fix the harmonic order; The maximum harmonic order N1 is dynamically adjusted based on the fundamental frequency.f When 1=50Hz, N1=15. f When 1=60Hz, N1=12; When calculating the harmonic content, harmonic components with amplitudes less than 5% of the fundamental frequency are removed to avoid the influence of weak interfering harmonics on the results. At the same time, the random error of frequency domain analysis is further reduced by taking the average value through multiple FFT transformations.
[0026] S4. Compare the content rate and total harmonic distortion rate of the target harmonic with the second threshold. If both the content rate and the total harmonic distortion rate of the target harmonic are higher than the second threshold, calculate the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic based on the content rate and the total harmonic distortion rate of the target harmonic, and compare it with the third threshold. Otherwise, return to re-acquire the signal. Specifically, in the second threshold setting, the target harmonic content threshold is 25%, and the total harmonic distortion (THD) threshold is 30%. In the above specific embodiment, the 3rd harmonic content is 30% and the 5th harmonic content is 20%, but at least one target harmonic content is higher than the threshold, and THD=43.8%, which satisfies the condition that both the target harmonic content and THD are higher than the corresponding second threshold, and step S6 is executed; if the 3rd harmonic content is 22% and THD=28% in a certain scenario, both are lower than the corresponding threshold, then return to step S1.
[0027] S5. If the peak-to-peak current fluctuation is greater than the third threshold, it is determined that there is electric bicycle charging behavior in the line. If the duration of this behavior is less than the preset time threshold, it is ignored; if the duration is greater than the time threshold, an alarm is issued or a power-off operation is performed.
[0028] Specifically, based on the content of the target harmonic and the total harmonic distortion rate, the calculation of the peak-to-peak current ripple rate of the target harmonic's current waveform includes: Calculate the peak-to-peak value of the current waveform ripple of the target harmonic. : ; in, I max This represents the maximum value of the current waveform. I min This represents the minimum value of the current waveform; Calculate the average current: ; In the formula, N 1 represents the maximum harmonic order considered. For the first k The current value of the first harmonic; Calculate the peak-to-peak current fluctuation rate (PPV): .
[0029] As a preferred example of the above embodiments, the first threshold and the second threshold are dynamic thresholds that are automatically adjusted based on the load level or peak value.
[0030] Specifically, the threshold value is adjusted based on the total current in the line. The threshold can be decreased under low load and increased under high load. ; In the formula, The first threshold, The initial reference value for the first threshold. This represents the total current in the power supply circuit. The second threshold includes the target harmonic content threshold and the total harmonic distortion rate threshold, specifically: The target harmonic content threshold = ; ; in, , , It is an empirical coefficient. The initial reference value representing the content of the target harmonic. The total harmonic distortion threshold; This is the initial reference value for the total harmonic distortion rate threshold. Dynamic thresholds can take into account different power consumption scenarios and adapt to situations with large load fluctuations and an unpredictable number of electric vehicles.
[0031] As a preferred example of the above embodiments, it further includes: establishing a sample library, which stores the typical power factor range and harmonic characteristic spectrum templates of different models of electric bicycle chargers, and matching the features calculated in real time with the templates in the sample library in step S5.
[0032] The method of this invention can realize real-time or near-real-time analysis of electricity consumption. Once the charging behavior of electric bicycles is identified, an early warning message can be sent to the property management, fire department or user immediately through Internet of Things technology, thereby realizing early warning and in-process intervention, eliminating fire hazards in the bud and greatly improving the level of safety management.
[0033] In addition to safety monitoring, the charging data identified by this invention (such as charging time, charging duration, and energy consumption) can be used for smart energy management in communities or cities, providing valuable data support for smart energy management decisions such as charging pile planning, off-peak charging incentives, and grid load regulation.
[0034] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides an electric vehicle charging identification device, comprising: The acquisition module is used to acquire AC voltage and current signals in the power supply circuit in real time; The calculation module is used to calculate the real-time power factor and power factor fluctuation rate based on the AC voltage signal and current signal. The first comparison module is used to compare the real-time power factor with a first threshold. If the real-time power factor is lower than the first threshold and the real-time power factor gradually increases within a preset time, a fast Fourier transform is performed on the total current signal to obtain the frequency domain signal, and the content rate of the target harmonic and the total harmonic distortion rate are screened based on the frequency domain signal; otherwise, the signal is reacquired. The second comparison block is used to compare the content rate and total harmonic distortion rate of the target harmonic with a second threshold. If both the content rate and the total harmonic distortion rate of the target harmonic are higher than the second threshold, the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic is calculated based on the content rate and the total harmonic distortion rate of the target harmonic and compared with a third threshold; otherwise, the signal is reacquired. The identification module is used to determine that there is electric bicycle charging behavior in the line if the peak-to-peak current fluctuation is greater than the third threshold. If the duration of the behavior is less than the preset time threshold, it is ignored; if the duration is greater than the time threshold, an alarm is issued or a power-off operation is performed.
[0035] The device's structure allows for non-intrusive and privacy-preserving operation. It only requires the installation of a single monitoring device (such as a smart meter or a dedicated sensor) at the main distribution box. Identification can be achieved by analyzing the electrical waveform characteristics of the main circuit. There is no need to enter the user's room or install cameras, completely avoiding the problem of infringing on user privacy, making it easier for users to accept and promote.
[0036] Moreover, it is low-cost and easy to deploy: the hardware required for this invention is an existing smart meter or the addition of a low-cost voltage / current sensor and signal processing unit, without the need for expensive high-speed sampling or image acquisition equipment. This makes the solution very suitable for large-scale community-based projects, renovations of old residential areas, and other similar projects, demonstrating high economic efficiency and feasibility.
[0037] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing an electric vehicle charging identification method; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.
[0038] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the electric vehicle charging identification method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.
[0039] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.
[0040] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.
[0041] The memory 101 in the electronic device 100 stores multiple instructions to implement an electric vehicle charging identification method, and the processor 102 can execute multiple instructions to achieve the following: Real-time acquisition of AC voltage and current signals in the power supply circuit; Calculate the real-time power factor based on the AC voltage and current signals; The real-time power factor is compared with the first threshold. If the real-time power factor is lower than the first threshold and gradually increases within a preset time, a fast Fourier transform is performed on the total current signal to obtain the frequency domain signal. The content rate of the target harmonic and the total harmonic distortion rate are then screened based on the frequency domain signal. Otherwise, the signal is reacquired. The content rate and total harmonic distortion rate of the target harmonic are compared with the second threshold. If both the content rate and the total harmonic distortion rate of the target harmonic are higher than the second threshold, the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic is calculated based on the content rate and the total harmonic distortion rate of the target harmonic and compared with the third threshold. Otherwise, the signal is reacquired. If the peak-to-peak current fluctuation exceeds the third threshold, it is determined that there is electric bicycle charging behavior in the line. If the duration of this behavior is less than the preset time threshold, it is ignored; if the duration is greater than the time threshold, an alarm is issued or a power-off operation is performed.
[0042] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).
[0043] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0044] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0045] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0046] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0047] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.
Claims
1. A method for identifying electric vehicle charging, characterized in that, include: Real-time acquisition of AC voltage and current signals in the power supply circuit; Calculate the real-time power factor based on the AC voltage and current signals; The real-time power factor is compared with the first threshold. If the real-time power factor is lower than the first threshold and gradually increases within a preset time, a fast Fourier transform is performed on the total current signal to obtain the frequency domain signal. The content rate of the target harmonic and the total harmonic distortion rate are then screened based on the frequency domain signal. Otherwise, the signal is reacquired. The content rate and total harmonic distortion rate of the target harmonic are compared with the second threshold. If both the content rate and the total harmonic distortion rate of the target harmonic are higher than the second threshold, the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic is calculated based on the content rate and the total harmonic distortion rate of the target harmonic and compared with the third threshold. Otherwise, return to reacquire the signal; If the peak-to-peak current fluctuation is greater than the third threshold, it is determined that there is electric bicycle charging behavior in the line, and if the duration of the behavior is less than the preset time threshold, it is ignored. If the duration exceeds the time threshold, an alarm will be issued or a power outage will be performed.
2. The electric vehicle charging identification method as described in claim 1, characterized in that, In the step of real-time acquisition of AC voltage and current signals in the power supply circuit, a window length of 1-2 power frequency cycles is used.
3. The electric vehicle charging identification method as described in claim 1, characterized in that, The step of calculating the real-time power factor based on the AC voltage signal and current signal includes: based on t Instantaneous active power is calculated from the voltage and current signals at a given time. The effective value of voltage is calculated based on the sampled voltage value, and the effective value of current is calculated based on the sampled current value; The average power within the sampling window is collected, and the real-time power factor is obtained by comparing the average power within the sampling window with the effective values of voltage and current.
4. The electric vehicle charging identification method as described in claim 1, characterized in that, The first and second thresholds are dynamic thresholds that are automatically adjusted based on load level or peak value.
5. The electric vehicle charging identification method as described in claim 1, characterized in that, The steps of performing a fast Fourier transform on the total current signal to obtain the frequency domain signal, and then screening the content of target harmonics and the total harmonic distortion rate based on the frequency domain signal include: The total current signal is subjected to a fast Fourier transform to obtain the frequency domain signal; Obtain the amplitude of the frequency domain signal, and calculate the harmonic frequencies based on the amplitude of the frequency domain signal. : ； in, n For harmonic order; f 1 represents the fundamental frequency, taken as 50. Hz ; Based on the fundamental frequency, calculate n Amplitude of the subharmonic component : ； based on n Amplitude of the subharmonic component I n and fundamental amplitude I 1. Calculate the target harmonic content; Calculate the total harmonic distortion (THD) based on the amplitude of each harmonic: ； In the formula, N 1 represents the maximum harmonic order considered. This represents the current amplitude of the fundamental frequency.
6. The electric vehicle charging identification method as described in claim 1, characterized in that, The calculation of the peak-to-peak current ripple rate of the target harmonic's current waveform pulsation based on the target harmonic's content and total harmonic distortion rate includes: Calculate the peak-to-peak value of the current waveform ripple of the target harmonic. I pp : ； in, I max This represents the maximum value of the current waveform. I min This represents the minimum value of the current waveform; Calculate the average current: ； In the formula, N 1 represents the maximum harmonic order considered. For the first The current value of the subharmonic; Calculate the peak-to-peak current fluctuation rate (PPV): 。 7. The electric vehicle charging identification method as described in claim 1, characterized in that, Also includes: A sample library is established, which stores typical power factor ranges and harmonic characteristic spectrum templates for different models of electric bicycle chargers. The features calculated in real time are matched with the templates in the sample library.
8. An electric vehicle charging identification device, characterized in that, include: The acquisition module is used to acquire AC voltage and current signals in the power supply circuit in real time; The calculation module is used to calculate the real-time power factor and power factor fluctuation rate based on the AC voltage signal and current signal. The first comparison module is used to compare the real-time power factor with a first threshold. If the real-time power factor is lower than the first threshold and the real-time power factor gradually increases within a preset time, a fast Fourier transform is performed on the total current signal to obtain the frequency domain signal, and the content rate of the target harmonic and the total harmonic distortion rate are screened based on the frequency domain signal; otherwise, the signal is reacquired. The second comparison block is used to compare the content rate of the target harmonic and the total harmonic distortion rate with the second threshold. If both the content rate of the target harmonic and the total harmonic distortion rate are higher than the second threshold, the peak-to-peak current fluctuation rate of the current waveform pulsation of the target harmonic is calculated based on the content rate of the target harmonic and the total harmonic distortion rate, and compared with the third threshold. Otherwise, return to reacquire the signal; The identification module is used to determine that there is electric bicycle charging behavior in the line if the peak-to-peak current fluctuation is greater than the third threshold, and to ignore it if the duration of the behavior is less than the preset time threshold. If the duration exceeds the time threshold, an alarm will be issued or a power outage will be performed.
9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the electric vehicle charging identification method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the electric vehicle charging identification method as described in any one of claims 1 to 7.