CN117929952B - Novel arc fault detection method for electric automobile charging pile - Google Patents
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Abstract
The invention provides a novel arc fault detection method of an electric automobile charging pile, which belongs to the technical field of charging piles and comprises the following steps: s1: respectively collecting normal working and real-time working electrical parameters of the charging pile, constructing an original data set, and obtaining a normal electric signal differential data feature set DIF0 according to the normal working electrical parameters; s2: processing and analyzing the data in the original data set to obtain a preprocessed data set; s3: screening the data in the preprocessed data set, and fusing the screened data with a neural network model to obtain a real-time electric signal differential data feature set DIF1; s4: and comparing and judging the DIF1 and the DIF0, and identifying arc faults. The invention can be suitable for accurately extracting the arc fault characteristics in the noise environment, reduces the influence of the shielding effect on the data in the current environment of the charging pile, can further improve the detection accuracy, and has higher detection accuracy.
Description
Technical Field
The invention belongs to the technical field of charging piles, and particularly relates to a novel arc fault detection method for an electric vehicle charging pile.
Background
The utility model provides an electric automobile fills electric pile and provides electric quantity supplementary energy device for electric automobile, can fix on ground or wall, installs in public building (charging station, mall, public parking area etc.) and residential area parking area, can charge for electric automobile of various models according to adjustment voltage and current. The input end of the charging pile is directly connected with an alternating current power grid, and the output end of the charging pile is provided with a charging plug for charging the electric automobile.
With the popularization of electric vehicles and the wide application of charging piles, the safety and reliability of the charging piles become one of important concerns. In the charging process of the electric automobile, arc faults can occur due to uncertain reasons, so that electric energy is wasted, and serious consequences such as equipment damage and even fire disaster can be caused seriously. Therefore, the detection and timely treatment of arc faults is particularly important.
The traditional arc fault detection method mainly detects arc characteristics based on amplitude and frequency changes of current and voltage signals, but the detection result is not accurate and reliable enough under the noise interference environment. In addition, the traditional detection method is also influenced by the current shielding effect in the charging environment, so that the detection effect of the arc faults is limited, and the accuracy of the arc fault detection is influenced.
Disclosure of Invention
In order to solve the problems in the prior art, a novel arc fault detection method for the electric vehicle charging pile is provided.
The technical scheme adopted for solving the technical problems is as follows:
the technical scheme provides a novel arc fault detection method of an electric automobile charging pile, which comprises the following steps:
S1: respectively collecting normal working and real-time working electric parameters of the charging pile, constructing an original data set according to the real-time working electric parameters, and obtaining a normal electric signal differential data feature set DIF0 after processing and analyzing according to the normal working electric parameters;
S2: processing and analyzing the data in the original data set, and obtaining real-time spectrum data by using variation modal decomposition and Fourier transformation to obtain a preprocessed data set;
s3: screening the data in the preprocessed data set through a comparison algorithm, and fusing the screened data with a neural network model to obtain a real-time electric signal differential data feature set DIF1;
S4: and comparing and judging the real-time electric signal differential data characteristic set DIF1 with the normal electric signal differential data characteristic set DIF0, and identifying the arc fault.
Preferably, in S2, the data processing analysis method includes:
s11: the Hilbert transformation is carried out on the components of different modes, and single-side frequency spectrum of an analysis signal is obtained through calculation:
（1）
In the method, in the process of the invention, For the impulse signal used in Hilbert transform,/>For the bias component under the corresponding modal component,Real-time current signals corresponding to the charging piles;
the single-side frequency spectrum of the analysis signal under the corresponding modal component can be obtained by (1) revising the offset of the impulse signal required by Hilbert transformation;
s12: adding an exponential term into each mode, adjusting the estimated center frequency, and adjusting the frequency of each mode component to a corresponding baseband:
（2）
In the method, in the process of the invention, For the expression of frequencies corresponding to different modal components,/>Is the imaginary unit in signal gradient calculation,/>The amount of time in calculating for the signal gradient;
s13: calculating the square of the two norms of the demodulation signal gradient, and finally obtaining the following variation problems from the bandwidths of all modes:
（3）
（4）
In the method, in the process of the invention, Representing partial differential calculation of the subsequent equation,/>Is the constraint quantity of the current data;
(3) The equation obtains the variation problem under the constraint of the equation (4) through regulating the square of the two norms of the signal gradient and partial differential calculation, and carries out Fourier transformation on the demodulated variation modal signal to finish the pretreatment of data, wherein, the design is set according to the actual current frequency of the charging pile 。
Preferably, in S3, the data screening method includes:
s21: determining positive and negative sample pairs using a data screening module;
S22: using CNN networks Obtaining the coding result, wherein,/>Output result for pooling layer of corresponding neural network,/>For the corresponding sample;
s23: converting the encoding result to a contrast loss space using hidden layer multi-layer perception:
（5）
Wherein, For the pooling layer to output the result as a comparative loss space function expression under the independent variable,/>Activating a function for Relu for use in contrast training,/>And/>And outputting results respectively for the corresponding codes.
Preferably, in S3, the neural network model fusion method includes:
s31: parallelly combining a CNN network and an LSTM network, adding Flattern layers after pooling layers to build a fused neural network model by using middle layer information;
The CNN network consists of an input layer, two-dimensional convolution layers, two separable convolution layers, two maximum pooling layers and two Flattern layers, and the LSTM network consists of an input layer, an LSTM layer and a Flattern layer;
s32: the sample S (x, y) obtained after comparison learning is in a bounded variation space Differential data fusion is carried out, and partial differential gradient operators/>And (3) performing integral operation to obtain an energy universal function of the differential mathematical model:
（6）
In the method, in the process of the invention, Is Lagrangian operator,/>And/>Samples after and before the contrast learning,Fractional derivative of the influence factor of the data signal,/>Is a norm unit;
the integral of the sample and the partial differential gradient operator under the comparative learning is used for obtaining the expression of the differential mathematical model energy universal function, and the signal detection equation is obtained through differential definition and Euler equation definition:
（7）
In the method, in the process of the invention, For a specific step value,/>To derive a factor,/>To accumulate the detection times,/>Representing the corresponding current detection times, and obtaining an expression based on a signal detection equation under a fused neural network model through accumulation of Euler equations and sample information under different times;
And comparing the learned sample data, and obtaining a real-time electric signal differential data feature set DIF1 through a fused neural network model.
Preferably, in S1, the normal electric signal differential data feature set DIF0 is sample data obtained by collecting current signals of a mass of charging piles during normal operation, spectrum data of the mass of charging piles during normal operation is obtained through spectrum analysis based on the collected sample data, and the normal electric signal differential data feature set DIF0 of the charging piles during normal operation is established.
Preferably, the electrical parameter is the operating current of the charging pile.
Compared with the prior art, the invention has the following advantages:
1. According to the invention, the spectrum data of the charging pile current signal is extracted by adopting variation modal decomposition and Fourier transformation, and the data is screened and preprocessed by improving a contrast learning algorithm, so that the method can adapt to the accurate extraction of arc fault characteristics in a noise environment, and the characteristics of a real-time electric signal are judged by constructing a fused neural network model and adopting a differential characteristic set, so that the influence of shielding effect on the data in the charging pile current environment is reduced, and the accuracy of detection can be further improved by judging the fused neural network model and the differential characteristic set, and the method has higher detection precision.
2. The method provided by the invention has higher detection accuracy and robustness, can be suitable for extracting the arc fault characteristics in a noise environment, reduces the influence of the shielding effect of the current environment of the charging pile on data, improves the accuracy and precision of arc fault detection, adopts an advanced method for judging a fused neural network model and a differential characteristic set, ensures that the detection result is more reliable and stable, improves the safety and stability of the operation of the charging pile, and has wide application prospects in the electric automobile industry.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
Fig. 1 is a schematic view of a charging pile according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As shown in fig. 1, this embodiment provides a novel arc fault detection method for an electric vehicle charging pile, including the following steps:
S1: respectively collecting normal working and real-time working electric parameters of the charging pile, constructing an original data set according to the real-time working electric parameters, and obtaining a normal electric signal differential data feature set DIF0 after processing and analyzing according to the normal working electric parameters;
S2: processing and analyzing the data in the original data set, and obtaining real-time spectrum data by using variation modal decomposition and Fourier transformation to obtain a preprocessed data set;
s3: screening the data in the preprocessed data set through a comparison algorithm, and fusing the screened data with a neural network model to obtain a real-time electric signal differential data feature set DIF1;
S4: and comparing and judging the real-time electric signal differential data characteristic set DIF1 with the normal electric signal differential data characteristic set DIF0, and identifying the arc fault.
S2, the data processing analysis method comprises the following steps:
s11: the Hilbert transformation is carried out on the components of different modes, and single-side frequency spectrum of an analysis signal is obtained through calculation:
（1）
In the method, in the process of the invention, For the impulse signal used in Hilbert transform,/>For the bias component under the corresponding modal component,Real-time current signals corresponding to the charging piles;
the single-side frequency spectrum of the analysis signal under the corresponding modal component can be obtained by (1) revising the offset of the impulse signal required by Hilbert transformation;
s12: adding an exponential term into each mode, adjusting the estimated center frequency, and adjusting the frequency of each mode component to a corresponding baseband:
（2）
In the method, in the process of the invention, For the expression of frequencies corresponding to different modal components,/>Is the imaginary unit in signal gradient calculation,/>The amount of time in calculating for the signal gradient;
(2) The formula realizes the adjustment of the component frequency of each mode by adding an index term to each mode;
s13: calculating the square of the two norms of the demodulation signal gradient, and finally obtaining the following variation problems from the bandwidths of all modes:
（3）
（4）
In the method, in the process of the invention, Representing partial differential calculation of the subsequent equation,/>Is the constraint quantity of the current data;
(3) The equation obtains the variation problem under the constraint of the equation (4) through regulating the square of the two norms of the signal gradient and partial differential calculation, and carries out Fourier transformation on the demodulated variation modal signal to finish the pretreatment of data, wherein, the design is set according to the actual current frequency of the charging pile 。
S3, the data screening method comprises the following steps:
s21: determining positive and negative sample pairs using a data screening module;
S22: using CNN networks Obtaining the coding result, wherein,/>Output result for pooling layer of corresponding neural network,/>For the corresponding sample;
s23: converting the encoding result to a contrast loss space using hidden layer multi-layer perception:
（5）
Wherein, For the pooling layer to output the result as a comparative loss space function expression under the independent variable,/>Activating a function for Relu for use in contrast training,/>And/>And outputting results respectively for the corresponding codes.
In S3, the neural network model fusion method comprises the following steps:
s31: parallelly combining a CNN network and an LSTM network, adding Flattern layers after pooling layers to build a fused neural network model by using middle layer information;
The CNN network consists of an input layer, two-dimensional convolution layers, two separable convolution layers, two maximum pooling layers and two Flattern layers, and the LSTM network consists of an input layer, an LSTM layer and a Flattern layer;
s32: the sample S (x, y) obtained after comparison learning is in a bounded variation space Differential data fusion is carried out, and partial differential gradient operators/>And (3) performing integral operation to obtain an energy universal function of the differential mathematical model:
（6）
In the method, in the process of the invention, Is Lagrangian operator,/>And/>Sample after and before contrast learning,/>, respectivelyFractional derivative of the influence factor of the data signal,/>Is a norm unit;
the integral of the sample and the partial differential gradient operator under the comparative learning is used for obtaining the expression of the differential mathematical model energy universal function, and the signal detection equation is obtained through differential definition and Euler equation definition:
（7）
In the method, in the process of the invention, For a specific step value,/>To derive a factor,/>To accumulate the detection times,/>Representing the corresponding current detection times, and obtaining an expression based on a signal detection equation under a fused neural network model through accumulation of Euler equations and sample information under different times;
And comparing the learned sample data, and obtaining a real-time electric signal differential data feature set DIF1 through a fused neural network model.
In the S1, the normal electric signal differential data feature set DIF0 is sample data obtained by collecting current signals of a mass of charging piles during normal operation, spectrum data during mass operation is obtained through spectrum analysis based on the collected sample data, and the normal electric signal differential data feature set DIF0 during normal operation of the charging piles is established, so that subsequent analogy judgment with fault arc data is facilitated.
With a 750V/120kW direct current charging pile as an implementation case, the cable, the connector and the port of the device can meet the high power transmission requirement. The detection device is shown in fig. 1, and fig. 1 is a schematic diagram of a charging pile structure according to the present invention. The direct current charging pile comprises a signal acquisition module, a signal processing module, a signal sending module and a voltage reduction and stabilization module, and is powered by alternating current 220V.
The mutual inductor on the surface of the equipment is used as a first ring of the signal acquisition module to introduce an electric signal to be detected and processed, the electric signal is judged through the signal processing module, if the electric signal is an arc signal, the signal sending module is used for alarming an arc fault, and meanwhile, a signal sending port outside the device is used for outputting the arc signal.
The direct current output part is connected with a signal acquisition module of the electric arc detection device, and a high-performance transformer acquires the output current signal of the charging pile in real time. The chassis shell is fixed on the wall, and aviation terminals at the lower end of the chassis shell are respectively connected with the transformer and the signal output port, the signal output port can output 485 signals, and a user can monitor and further process the signals in real time.
In the charging process of the electric automobile, the electric signal is monitored in real time, and the electric signal is judged through a high-performance chip. The collected current data is subjected to variation modal decomposition and Fourier transformation to obtain current spectrum data, the data is further screened by a comparison learning algorithm after data preprocessing, the screened data is subjected to fusion neural network model to obtain a real-time electric signal differential data feature set DIF1, and arc fault identification can be realized by comparing and judging the real-time electric signal differential data feature set DIF1 with a large number of normal electric signal differential data feature sets DIF0 under normal work in advance.
When no arc is generated, the high-performance chip does not judge the arc signal as an arc signal, and the arc detection device does not generate further actions; when the high-performance chip judges the electric signal as an arc signal, the signal sending module sends out a specific threshold condition to indicate that an arc fault occurs in the charging process, the buzzer continuously alarms, and the signal output port continuously outputs the arc signal. Therefore, the user can timely receive the fault prompt, and is convenient to take corresponding measures, such as stopping charging, reporting repair and the like.
The detection method provides an effective fault detection and protection strategy for the charging process of the charging pile of the direct-current 750V/120kW electric automobile, can ensure the safety of the charging pile and the charging equipment, and improves the charging experience of users.
The method has the advantages that the spectral data of the current signal of the charging pile is extracted through variation modal decomposition and Fourier transformation, the data is screened and preprocessed through improvement of a contrast learning algorithm, the method can adapt to accurate extraction of arc fault characteristics in a noise environment, the characteristics of real-time electric signals are judged through construction of a fused neural network model and adoption of a differential characteristic set, the influence of shielding effect on the data in the current environment of the charging pile is reduced, and the accuracy of detection can be further improved through judgment of the fused neural network model and the differential characteristic set, and the method has higher detection accuracy.
Meanwhile, the detection method has higher detection accuracy and robustness, can be suitable for extracting the arc fault characteristics in a noise environment, reduces the influence of the shielding effect of the current environment of the charging pile on data, improves the accuracy and precision of arc fault detection, adopts an advanced judgment method of fusing a neural network model and a differential characteristic set, ensures that the detection result is more reliable and stable, improves the safety and stability of the operation of the charging pile, and has wide application prospect in the electric automobile industry.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (3)
1. The novel arc fault detection method for the electric automobile charging pile is characterized by comprising the following steps of:
S1: respectively collecting normal working and real-time working electric parameters of the charging pile, constructing an original data set according to the real-time working electric parameters, and obtaining a normal electric signal differential data feature set DIF0 after processing and analyzing according to the normal working electric parameters;
S2: processing and analyzing the data in the original data set, and obtaining real-time spectrum data by using variation modal decomposition and Fourier transformation to obtain a preprocessed data set;
s3: screening the data in the preprocessed data set through a comparison algorithm, and fusing the screened data with a neural network model to obtain a real-time electric signal differential data feature set DIF1;
S4: comparing and judging the real-time electric signal differential data feature set DIF1 with the normal electric signal differential data feature set DIF0, and identifying arc faults;
S2, the data processing analysis method comprises the following steps:
s11: the Hilbert transformation is carried out on the components of different modes, and single-side frequency spectrum of an analysis signal is obtained through calculation:
（1）
In the method, in the process of the invention, For the impulse signal used in Hilbert transform,/>For the bias component under the corresponding modal component,/>Real-time current signals corresponding to the charging piles;
the single-side frequency spectrum of the analysis signal under the corresponding modal component can be obtained by (1) revising the offset of the impulse signal required by Hilbert transformation;
s12: adding an exponential term into each mode, adjusting the estimated center frequency, and adjusting the frequency of each mode component to a corresponding baseband:
（2）
In the method, in the process of the invention, For the expression of frequencies corresponding to different modal components,/>Is the imaginary unit in signal gradient calculation,/>The amount of time in calculating for the signal gradient;
s13: calculating the square of the two norms of the demodulation signal gradient, and finally obtaining the following variation problems from the bandwidths of all modes:
（3）
（4）
In the method, in the process of the invention, Representing partial differential calculation of the subsequent equation,/>Is the constraint quantity of the current data;
(3) The equation obtains the variation problem under the constraint of the equation (4) through regulating the square of the two norms of the signal gradient and partial differential calculation, and carries out Fourier transformation on the demodulated variation modal signal to finish the pretreatment of data, wherein, the design is set according to the actual current frequency of the charging pile ；
S3, the data screening method comprises the following steps:
s21: determining positive and negative sample pairs using a data screening module;
S22: using CNN networks Obtaining the coding result, wherein,/>Output result for pooling layer of corresponding neural network,/>For the corresponding sample;
s23: converting the encoding result to a contrast loss space using hidden layer multi-layer perception:
（5）
Wherein, For the pooling layer to output the result as a comparative loss space function expression under the independent variable,/>Activating a function for Relu for use in contrast training,/>And/>Respectively outputting results of corresponding codes;
in S3, the neural network model fusion method comprises the following steps:
s31: parallelly combining a CNN network and an LSTM network, adding Flattern layers after pooling layers to build a fused neural network model by using middle layer information;
The CNN network consists of an input layer, two-dimensional convolution layers, two separable convolution layers, two maximum pooling layers and two Flattern layers, and the LSTM network consists of an input layer, an LSTM layer and a Flattern layer;
s32: the sample S (x, y) obtained after comparison learning is in a bounded variation space Differential data fusion is carried out, and partial differential gradient operators/>And (3) performing integral operation to obtain an energy universal function of the differential mathematical model:
（6）
In the method, in the process of the invention, Is Lagrangian operator,/>And/>Sample after and before contrast learning,/>, respectivelyFractional derivative of the influence factor of the data signal,/>Is a norm unit;
the integral of the sample and the partial differential gradient operator under the comparative learning is used for obtaining the expression of the differential mathematical model energy universal function, and the signal detection equation is obtained through differential definition and Euler equation definition:
（7）
In the method, in the process of the invention, For a specific step value,/>To derive a factor,/>To accumulate the detection times,/>Representing the corresponding current detection times, and obtaining an expression based on a signal detection equation under a fused neural network model through accumulation of Euler equations and sample information under different times;
And comparing the learned sample data, and obtaining a real-time electric signal differential data feature set DIF1 through a fused neural network model.
2. The novel arc fault detection method for the electric vehicle charging pile according to claim 1, wherein in S1, the normal electric signal differential data feature set DIF0 is sample data obtained by collecting current signals of a mass of charging piles during normal operation, spectrum data of the mass of charging piles during normal operation is obtained through spectrum analysis based on the collected sample data, and the normal electric signal differential data feature set DIF0 of the charging piles during normal operation is established.
3. The novel arc fault detection method for an electric vehicle charging pile according to claim 1, wherein the electrical parameter is an operating current of the charging pile.
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