CN111025041A - Electric vehicle charging pile monitoring method and system, computer equipment and medium - Google Patents
Electric vehicle charging pile monitoring method and system, computer equipment and medium Download PDFInfo
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Abstract
The invention provides a method and a system for monitoring an electric vehicle charging pile, computer equipment and a medium, which comprise the following steps: acquiring a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process; inputting the first time sequence into a pre-trained extreme learning machine model to predict the power factor of the charging pile in the future time, and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order; acquiring a power factor critical value corresponding to the condition that the charging pile is degraded to be abnormal; and determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value. The monitoring system can monitor the state of the electric vehicle charging pile and determine whether to maintain the charging pile according to the monitoring data.
Description
Technical Field
The invention relates to the field of charging piles, in particular to a method and a system for monitoring a charging pile of an electric automobile, computer equipment and a medium.
Background
With the development of electric automobile industry in China, the sale and maintenance quantity of electric automobiles is continuously increased, and the matched charging service is also greatly improved. The charging pile is the most widely used charging equipment for the electric automobile, and plays an irreplaceable important role in supplying power energy sources for the electric automobile and promoting popularization of the electric automobile. Under the background, it is very important to evaluate and predict the state of the charging device in time and to perform maintenance according to the state of the charging device in order to ensure safe and stable operation of the charging device.
At present, some charging equipment manufacturers are matched with monitoring systems for charging piles, and have the functions of acquiring and storing state parameters of the charging piles in real time, however, the parameters are often only used for real-time observation and display and then stored in a database, and further effective and full utilization is not achieved. On the other hand, although the country combines the actual development of electric vehicle charging equipment in China, a plurality of national standards related to the charging equipment are released in 2016, and the new standards also increase the regulations on the aspect of charging safety, the monitoring standards and the monitoring methods for the universal key parameters of the charging pile are not provided. At present, the health management mode of the electric vehicle charging equipment still remains in the stage of regular maintenance or after-fault maintenance. This kind of maintenance mode efficiency is not high, and the means is laggard, and carries out periodic maintenance to healthy electric pile and also can lead to the waste of maintenance resource, is unfavorable for the economic benefits of the electric pile is filled in the maximize performance.
Disclosure of Invention
The invention aims to provide an electric vehicle charging pile monitoring method and system, computer equipment and a medium thereof, so as to monitor the state of an electric vehicle charging pile and determine whether to maintain the charging pile according to monitoring data.
In a first aspect, an embodiment of the present invention provides a method for monitoring a charging pile of an electric vehicle, including the following steps:
acquiring a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process;
inputting the first time sequence into a pre-trained extreme learning machine model to predict the power factor of the charging pile in the future time, and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order;
acquiring a power factor critical value corresponding to the condition that the charging pile is degraded to be abnormal;
and determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value.
Inputting the first time sequence into a pre-trained extreme learning machine model for prediction and outputting a second time sequence comprises the following steps:
step S101, taking the first time sequence as an input time sequence
Inputting the extreme learning machine model for processing to obtain an output time sequence
Step S102, time sequence
And
combining to form a new vector as an input time series of the new model
Returning to the step S1 according to the preset iteration times to carry out an iteration loop;
step S103, obtaining an input time sequence according to multiple iterationsColumn(s) of
A second time series is obtained.
Wherein the extreme learning machine model training process is as follows:
step 201, generating training and testing data by using historical monitoring data of a charging pile, and sampling a time sequence of the monitoring data at intervals according to a certain interval e, wherein the sampling length is t + d, (t)>d) Splitting each sampled sequence into one sample input xin＝{x1,x2,...,xtAnd the corresponding output xout＝{xt+1,xt+2,...,xt+dThen each sample is x ═ xin,xoutDividing all samples into a training set and a testing set according to a preset proportion;
step 202, setting the number of input nodes, hidden nodes and output nodes of the extreme learning machine to establish an extreme learning machine model; wherein, the number of input nodes is the dimension of training sample input, namely t; the number of output nodes is the dimension d of the training sample output; the number of nodes of the hidden layer is initially n-10 d, and a weight matrix W and a bias vector b between the input layer and the hidden layer are initialized randomly by normal distribution;
step 203, training the established extreme learning machine model by using the training sample set x according to the batch processing mode of the extreme learning machine, and calculating the output weight value of the extreme learning machine model
Wherein, H ═ g (W.X + b) is the hidden layer output, I is the diagonal matrix, C is the preset value;
step 204, inputting the input time sequence of the test sample into the established extreme learning machine model, and calculating the output time sequence of the extreme learning machine model; comparing the predicted time sequence with the output time sequence of the test sample to obtain the root mean square error of the predicted time sequence and the output time sequence of the test sample; if the root mean square error meets the preset precision threshold, the training of the extreme learning machine model is completed, otherwise, the number n of the nodes of the hidden layer is increased to n +100, and the step 202 is returned.
Wherein, the obtaining of the power factor critical value corresponding to the degradation of the charging pile to the abnormality includes:
step 301, give initial normal time sequence { XnormalAnd then the time series { X } which needs to be judgednew}；
Step 302, calculating the Mean and variance Var of the normal time sequence;
step 303, comparing the time series { X ] one by using LayAda criterionnewEach value x innew_iDifference from Mean;
step 304, if the difference value satisfies | xnew_i-Mean|<3Var, then the new value xnew_iAdding to the original normal time series, i.e. { Xnormal}＝{Xnormal,xnew_iI is made to be i +1 and returns to step 302; if xnew_i-Mean|>3Var, then i ═ i +1, and proceed to step 305;
step 305, compare the value x using the Lavian criterionnew_iAnd the difference value of the Mean value, if the difference value satisfies | xnew_i-Mean|<3Var, then the value xnew_iAdding the original normal sequence and returning to the step 302; if xnew_i-Mean|>3Var, then the value xnew_iIs counted as a power factor critical value.
Wherein, the determining whether to maintain the charging pile according to the second time series and the power factor critical value comprises:
determining a state change time sequence corresponding to the power factor critical value in the second time sequence;
and judging whether the charging time of the charging pile reaches the state change time sequence, if so, determining that the charging pile needs to be maintained, and if not, determining that the charging pile does not need to be maintained.
Wherein the method further comprises:
acquiring charging monitoring data of a charging pile;
and generating a first time sequence according to the charging monitoring data.
In a second aspect, an embodiment of the present invention provides an electric vehicle charging pile monitoring system, which is used for implementing the electric vehicle charging pile monitoring method according to the embodiment, and includes:
a sequence acquisition unit configured to acquire a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process;
the sequence prediction unit is used for inputting the first time sequence into a pre-trained extreme learning machine model to predict the power factor of the charging pile in the future time and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order;
the critical value obtaining unit is used for obtaining a corresponding power factor critical value when the charging pile degrades to be abnormal;
and the maintenance judging unit is used for determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value.
Wherein the system further comprises:
the monitoring data acquisition unit is used for acquiring charging monitoring data of the charging pile;
and the sequence generating unit is used for generating a first time sequence according to the charging monitoring data.
In a third aspect, an embodiment of the present invention provides a computer device, including: the electric vehicle charging pile monitoring system is characterized by comprising an electric vehicle charging pile monitoring system; or, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the electric vehicle charging pile monitoring method according to the embodiment.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the electric vehicle charging pile monitoring method according to the embodiment.
The embodiment of the invention provides a method and a system for monitoring an electric vehicle charging pile, computer equipment and a medium, which only need to provide historical data of the charging pile, learn from the historical data by using an extreme learning machine and acquire the state change trend of the charging pile. Because the monitoring parameters of the actual charging pile truly reflect various factors such as the electrical characteristics, the use characteristics, the environment and the like, a mathematical model or other information related to the equipment is not required to be established any more, and the monitoring data of the state parameters of the charging pile can be applied as long as enough monitoring data are available, so that the embodiment of the invention has good universality and can be widely applied to various charging pile equipment.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a monitoring method for an electric vehicle charging pile according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a charging pile power factor curve according to an embodiment of the invention.
Fig. 3 is a schematic diagram of a training process of a limit learning machine according to an embodiment of the present invention.
Fig. 4 is a schematic flowchart of step S300 according to an embodiment of the present invention.
Fig. 5 is a schematic flow chart of a monitoring system for an electric vehicle charging pile according to a second embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures closely related to the solution according to the present invention are shown in the drawings, and other details not closely related to the present invention are omitted.
Example one
The embodiment of the invention provides a method for monitoring a charging pile of an electric vehicle, and fig. 1 is a schematic flow chart of the method in the embodiment of the invention, and with reference to fig. 1, the method comprises the following steps:
s100, acquiring a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process;
specifically, as shown in fig. 2, the first time series is known data, the average power factor value of each charging is obtained by monitoring the charging state of the charging pile, and then the charging sequence is arranged to obtain the first time series, where the abscissa of the point on the curve in fig. 2 represents the number of charging times and the ordinate represents the average power factor value.
S200, inputting the first time sequence into a pre-trained extreme learning machine model to predict a power factor value of a charging pile in the future time, and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order;
specifically, as shown in fig. 2, the second time series is prediction data indicating how many corresponding charging pile power factor values are for each subsequent charging on the basis of the first time series.
And step S300, acquiring a power factor critical value corresponding to the condition that the charging pile is degraded to be abnormal.
Specifically, the power factor threshold refers to that when the power factor of the charging pile is reduced to the power factor threshold, the state of the charging pile is degraded to be abnormal, and at this time, the charging pile needs to be maintained.
And S400, determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value.
Specifically, when the power factor of the charging pile is decreased to the power factor critical value, as shown in fig. 2, the corresponding charging times reach 556 times, and therefore, by monitoring the charging times of the charging pile, when the charging times reach 556 times or approach 556 times, the charging pile is maintained.
Wherein the method further comprises:
acquiring charging monitoring data of a charging pile;
and generating a first time sequence according to the charging monitoring data.
Specifically, the charging pile is used for acquiring t points from the latest point to the front as a first time sequence of a model according to the current parameter monitoring data
Each point represents a charge, the value of the point represents the average power factor value during the charge, and the last charge is the most recent point.
Wherein, the step S200 includes:
step S101, the first time sequence is carried out
Inputting the extreme learning machine model as an input time sequence to be processed to obtain an output time sequence
Step S102, time sequence
And
combining to form a new vector, and taking t points from back to front as an input time sequence of a new model
Input time series as a new model
Returning to the step S1 for iteration circulation according to the preset iteration times until the iteration times are finished;
step S103, obtaining an input time sequence according to multiple iterations
A second time series is obtained.
Referring to fig. 3, the extreme learning machine model training process is as follows:
step 201, generating training and testing data by using historical monitoring data of a charging pile, and sampling a time sequence of the monitoring data at intervals according to a certain interval e, wherein the sampling length is t + d, (t)>d) Splitting each sampled sequence into one sample input xin＝{x1,x2,...,xtAnd the corresponding output xout＝{xt+1,xt+2,...,xt+dThen each sample is x ═ xin,xoutDividing all samples into a training set and a testing set according to a preset proportion;
for example, since the power factor of the charging pile has a great influence on the charging efficiency and the charging time, the health state of the charging pile can be reflected, and therefore, historical monitoring data of the parameters are selected as training and test sample data. The input of the sample is 40 dimensions, namely the average power factor of 40 charging times continuously, and the output is the average power factor of 10 charging times next for the 40 charging times. 500 points of historical monitoring data of the power factor of the charging pile are selected to be used as training and testing samples. With the first 400 data as training sets and the last 100 as test sets, 199 training samples and 49 test samples can be generated.
Step 202, setting the number of input nodes, hidden nodes and output nodes of the extreme learning machine to establish an extreme learning machine model; wherein, the number of input nodes is the dimension of training sample input, namely t; the number of output nodes is the dimension d of the training sample output; the number of nodes of the hidden layer is initially n-10 d, and a weight matrix W and a bias vector b between the input layer and the hidden layer are initialized randomly by normal distribution;
for example, 40 input nodes and 10 output nodes of the extreme learning machine are set. And (5) setting the number n of the initial hidden layer nodes to be 400, and then randomly initializing a weight matrix W and a bias vector b between the input layer and the hidden layer by utilizing normal distribution. And giving a preset precision threshold errth＝0.0001。
Step 203, training the established extreme learning machine model by using the training sample set X according to the batch processing mode of the extreme learning machine, and calculating the output weight value of the extreme learning machine model
Where H ═ g (W.X + b) is the hidden layer output, I is the diagonal matrix, and C is a constant.
Step 204, inputting the input time sequence of the test sample into the established extreme learning machine model, and calculating the output time sequence of the extreme learning machine model; comparing the predicted time sequence with the output time sequence of the test sample to obtain the root mean square error of the predicted time sequence and the output time sequence of the test sample; if the root mean square error meets the preset precision threshold, the training of the extreme learning machine model is completed, otherwise, the number n of the nodes of the hidden layer is increased to n +100, and the step 202 is returned.
For example, the predicted time series is compared with the output of the test sample, and the root mean square error of the two is calculated. In the case of n being 400, the calculated accuracy err being 0.0000554<errthThe requirement is met, so that the number of nodes of the hidden layer does not need to be increased, and the training is finished.
In one embodiment, referring to fig. 4, the step S300 includes:
step 301, give initial normal time sequence { XnormalAnd then the time series { X } which needs to be judgednew}；
Step 302, calculating the Mean and variance Var of the normal time sequence;
step 303, comparing the time series { X ] one by using LayAda criterionnewEach value x innew_iDifference from Mean;
step 304, if the difference value satisfies | xnew_i-Mean|<3Var, then the new value xnew_iAdding to the original normal time series, i.e. { Xnormal}＝{Xnormal,xnew_iI is made to be i +1 and returns to step 302; if xnew_i-Mean|>3Var, then i ═ i +1, and proceed to step 305;
step 305, compare the value x using the Lavian criterionnew_iThe difference value of the Mean value is smaller, and | x is satisfiednew_i-Mean|<3Var, then the value xnew_iAdding the original normal sequence and returning to the step 302; if | x is satisfiednew_i-Mean|>3Var, then the value xnew_iPower factor threshold value: x is the number ofcr＝xnew_i。
Specifically, with the continuous operation of the charging pile, the situations of equipment loss, aging and the like occur, the equipment state is judged in several stages from normal operation to abnormal operation to failure and the like according to the state parameters, however, the range of the parameter values is normal, the range of the parameter values is abnormal, and the parameter values are obtained by a large amount of experiments or by a specialist with strong experience, and sometimes it is difficult to provide such a critical value. The embodiment provides the Lauda criterion of mathematical statistics for judgment, is convenient to implement, and solves the problem that the analysis parameters need experience or experiments.
Wherein the step S400 includes:
step 401, determining a state change time sequence corresponding to the power factor and the power factor critical value in the second time sequence;
and step 402, judging whether the charging time of the charging pile reaches the state change time sequence, if so, determining that the charging pile needs to be maintained, and if not, determining that the charging pile does not need to be maintained.
Specifically, referring to FIG. 2, the data of 200 points in front can be taken as { XnormalFrom point 201 to 600 as { X }newAnd point 556 is a critical point, when the charging reaches the 556 th time, the state of the charging pile is degraded to be abnormal, and the charging pile is recommended to be subjected to maintenance and inspection.
According to the method, special mathematical modeling is not needed for the charging piles of various models, or the state turning points of the state parameters of the charging piles are determined through tests, only historical data of the monitoring parameters of the charging piles are needed to be provided, and a data training extreme learning machine model is utilized, so that health information contained in the data is mined, the future state of the charging piles is predicted, corresponding maintenance suggestions are given, and the automation level of health management of the charging piles is effectively improved.
Example two
As shown in fig. 5, a second embodiment of the present invention provides an electric vehicle charging pile monitoring system, which is used for implementing the electric vehicle charging pile monitoring method according to the first embodiment, and includes:
a sequence acquisition unit 1 for acquiring a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process;
the sequence prediction unit 2 is used for inputting the first time sequence into a pre-trained extreme learning machine model to predict the power factor of the charging pile in the future time and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order;
the critical value obtaining unit 3 is used for obtaining a power factor critical value corresponding to the charging pile when the charging pile degrades to be abnormal;
and the maintenance judging unit 4 is used for determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value.
In one embodiment, the method further comprises:
the monitoring data acquisition unit 5 is used for acquiring charging monitoring data of the charging pile;
and the sequence generating unit 6 is used for generating a first time sequence according to the charging monitoring data.
It should be noted that the system according to the second embodiment corresponds to the method according to the first embodiment, and therefore, a part of the system according to the second embodiment that is not described in detail can be obtained by referring to the content of the method according to the first embodiment, and is not described again here.
It is to be noted that, based on the content, those skilled in the art can clearly understand that the embodiments of the present invention can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to implement the methods/systems described in the foregoing embodiments.
EXAMPLE III
An embodiment of the present invention provides a computer device, including: the electric vehicle charging pile monitoring system is characterized by comprising an electric vehicle charging pile monitoring system; or, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the electric vehicle charging pile monitoring method according to the embodiment.
Of course, the computer device may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and the computer device may also include other components for implementing the functions of the device, which are not described herein again.
Example four
The fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for monitoring a charging pile of an electric vehicle according to the first embodiment.
The foregoing is directed to embodiments of the present invention, and it is understood that various modifications and improvements can be made by those skilled in the art without departing from the spirit of the invention.
Claims (10)
1. The method for monitoring the charging pile of the electric automobile is characterized by comprising the following steps of:
acquiring a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process;
inputting the first time sequence into a pre-trained extreme learning machine model to predict the power factor of the charging pile in the future time, and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order;
acquiring a power factor critical value corresponding to the condition that the charging pile is degraded to be abnormal;
and determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value.
2. The method for monitoring the charging pile of the electric vehicle according to claim 1, wherein inputting the first time sequence into a pre-trained extreme learning machine model for predicting and outputting a second time sequence comprises:
step S101, taking the first time sequence as an input time sequence
Inputting the extreme learning machine model for processing to obtain an output time sequence
Step S102, time sequence
And
combining to form a new vector as an input time series of the new model
Returning to the step S1 according to the preset iteration times to carry out an iteration loop;
step S103, obtaining an input time sequence according to multiple iterations
A second time series is obtained.
3. The electric vehicle charging pile monitoring method according to claim 1, wherein the extreme learning machine model training process is as follows:
step 201, generating training and testing data by using historical monitoring data of a charging pile, and sampling a time sequence of the monitoring data at intervals according to a certain interval e, wherein the sampling length is t + d, (t)>d) Splitting each sampled sequence into one sample input xin＝{x1,x2,...,xtAnd the corresponding output xout＝{xt+1,xt+2,...,xt+dThen each sample is x ═ xin,xoutDividing all samples into a training set and a testing set according to a preset proportion;
step 202, setting the number of input nodes, hidden nodes and output nodes of the extreme learning machine to establish an extreme learning machine model; wherein, the number of input nodes is the dimension of training sample input, namely t; the number of output nodes is the dimension d of the training sample output; the number of nodes of the hidden layer is initially n-10 d, and a weight matrix W and a bias vector b between the input layer and the hidden layer are initialized randomly by normal distribution;
step 203, training the established extreme learning machine model by using the training sample set x according to the batch processing mode of the extreme learning machine, and calculating the output weight value of the extreme learning machine model
Wherein, H ═ g (W.X + b) is the hidden layer output, I is the diagonal matrix, C is the preset value;
step 204, inputting the input time sequence of the test sample into the established extreme learning machine model, and calculating the output time sequence of the extreme learning machine model; comparing the predicted time sequence with the output time sequence of the test sample to obtain the root mean square error of the predicted time sequence and the output time sequence of the test sample; if the root mean square error meets the preset precision threshold, the training of the extreme learning machine model is completed, otherwise, the number n of the nodes of the hidden layer is increased to n +100, and the step 202 is returned.
4. The method for monitoring the charging pile of the electric vehicle according to claim 1, wherein the step of acquiring the power factor critical value corresponding to the charging pile degraded to an abnormal state comprises the following steps:
step 301, give initial normal time sequence { XnormalAnd then the time series { X } which needs to be judgednew}；
Step 302, calculating the Mean and variance Var of the normal time sequence;
step 303, comparing the time series { X ] one by using LayAda criterionnewEach value x innew_iDifference from Mean;
step 304, if the difference value satisfies | xnew_i-Mean|<3Var, then the new value xnew_iAdding to the original normal time series, i.e. { Xnormal}＝{Xnormal,xnew_iI is made to be i +1 and returns to step 302; if xnew_i-Mean|>3Var, then i ═ i +1, and proceed to step 305;
step 305, compare the value x using the Lavian criterionnew_iAnd the difference value of the Mean value, if the difference value satisfies | xnew_i-Mean|<3Var, then the value xnew_iAdding the original normal sequence and returning to the step 302; if xnew_i-Mean|>3Var, then the value xnew_iIs counted as a power factor critical value.
5. The method for monitoring the charging post of the electric vehicle according to claim 1, wherein the determining whether the charging post needs to be repaired according to the second time series and the power factor threshold value comprises:
determining a state change time sequence corresponding to the power factor critical value in the second time sequence;
and judging whether the charging time of the charging pile reaches the state change time sequence, if so, determining that the charging pile needs to be maintained, and if not, determining that the charging pile does not need to be maintained.
6. The electric vehicle charging pile monitoring method according to claim 1, further comprising:
acquiring charging monitoring data of a charging pile;
and generating a first time sequence according to the charging monitoring data.
7. An electric vehicle charging pile monitoring system for realizing the electric vehicle charging pile monitoring method of any one of claims 1 to 5, comprising:
a sequence acquisition unit configured to acquire a first time sequence; the first time sequence comprises a plurality of average power factor values which are sequenced in the past time according to the charging time sequence, wherein each average power factor value refers to the average power factor value of the charging pile in each charging process;
the sequence prediction unit is used for inputting the first time sequence into a pre-trained extreme learning machine model to predict the power factor of the charging pile in the future time and outputting a second time sequence; the second time series includes a plurality of average power factor values in a future time ordered in a charging time order;
the critical value obtaining unit is used for obtaining a corresponding power factor critical value when the charging pile degrades to be abnormal;
and the maintenance judging unit is used for determining whether the charging pile needs to be maintained or not according to the second time sequence and the power factor critical value.
8. The electric vehicle charging pile monitoring system of claim 7, further comprising:
the monitoring data acquisition unit is used for acquiring charging monitoring data of the charging pile;
and the sequence generating unit is used for generating a first time sequence according to the charging monitoring data.
9. A computer device, comprising: the electric vehicle charging pile monitoring system according to any one of claims 7-8; or, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the electric vehicle charging pile monitoring method according to any one of claims 1-6.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements the steps of the method for monitoring an electric vehicle charging pile according to any one of claims 1-6.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112100920A (en) * | 2020-09-15 | 2020-12-18 | 东南大学 | Power distribution network three-phase voltage calculation method, device, equipment and storage medium |
| CN112433122A (en) * | 2020-11-23 | 2021-03-02 | 广州橙行智动汽车科技有限公司 | Charging pile available state detection method, device, equipment and storage medium |
| CN115542235A (en) * | 2022-11-07 | 2022-12-30 | 北京志翔科技股份有限公司 | Method, device and equipment for determining metering error of charging gun and storage medium |
| CN117074840A (en) * | 2023-10-16 | 2023-11-17 | 南京思宇电气技术有限公司 | DC charging pile on-line testing system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102944769A (en) * | 2012-10-18 | 2013-02-27 | 上海市电力公司 | Fault diagnosis method of power transformer based on extreme learning machine |
| CN105046374A (en) * | 2015-08-25 | 2015-11-11 | 华北电力大学 | Power interval predication method based on nucleus limit learning machine model |
| CN105354646A (en) * | 2015-12-04 | 2016-02-24 | 福州大学 | Power load forecasting method for hybrid particle swarm optimization and extreme learning machine |
| DE102016106776A1 (en) * | 2015-04-16 | 2016-10-20 | Ford Global Technologies, Llc | Vehicle high voltage system insulation test |
| CN106446440A (en) * | 2016-10-11 | 2017-02-22 | 天津大学 | Short-term photovoltaic generation power prediction method based on online sequential extreme learning machine |
| CN107589323A (en) * | 2017-08-30 | 2018-01-16 | 安徽千里眼信息科技有限公司 | A kind of charging pile electric energy quality monitoring and charging managing and control system |
| CN108287317A (en) * | 2018-01-24 | 2018-07-17 | 上海电气分布式能源科技有限公司 | Power of battery prediction model generation method and system, power forecasting method and system |
| CN108761346A (en) * | 2018-06-20 | 2018-11-06 | 首都师范大学 | A kind of vehicle lithium battery method for predicting residual useful life |
| WO2019035770A1 (en) * | 2017-08-18 | 2019-02-21 | Nanyang Technological University | Adaptive computer system, and methods and apparatus for training the adaptive computer system |
| CN110133395A (en) * | 2019-04-04 | 2019-08-16 | 国网电动汽车服务有限公司 | A kind of charging pile health status diagnostic method and device |
| CN110333462A (en) * | 2019-08-08 | 2019-10-15 | 首都师范大学 | A kind of lithium ion battery life-span prediction method under random discharge environment based on DGWO-ELM |
-
2019
- 2019-11-07 CN CN201911081849.5A patent/CN111025041A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102944769A (en) * | 2012-10-18 | 2013-02-27 | 上海市电力公司 | Fault diagnosis method of power transformer based on extreme learning machine |
| DE102016106776A1 (en) * | 2015-04-16 | 2016-10-20 | Ford Global Technologies, Llc | Vehicle high voltage system insulation test |
| CN105046374A (en) * | 2015-08-25 | 2015-11-11 | 华北电力大学 | Power interval predication method based on nucleus limit learning machine model |
| CN105354646A (en) * | 2015-12-04 | 2016-02-24 | 福州大学 | Power load forecasting method for hybrid particle swarm optimization and extreme learning machine |
| CN106446440A (en) * | 2016-10-11 | 2017-02-22 | 天津大学 | Short-term photovoltaic generation power prediction method based on online sequential extreme learning machine |
| WO2019035770A1 (en) * | 2017-08-18 | 2019-02-21 | Nanyang Technological University | Adaptive computer system, and methods and apparatus for training the adaptive computer system |
| CN107589323A (en) * | 2017-08-30 | 2018-01-16 | 安徽千里眼信息科技有限公司 | A kind of charging pile electric energy quality monitoring and charging managing and control system |
| CN108287317A (en) * | 2018-01-24 | 2018-07-17 | 上海电气分布式能源科技有限公司 | Power of battery prediction model generation method and system, power forecasting method and system |
| CN108761346A (en) * | 2018-06-20 | 2018-11-06 | 首都师范大学 | A kind of vehicle lithium battery method for predicting residual useful life |
| CN110133395A (en) * | 2019-04-04 | 2019-08-16 | 国网电动汽车服务有限公司 | A kind of charging pile health status diagnostic method and device |
| CN110333462A (en) * | 2019-08-08 | 2019-10-15 | 首都师范大学 | A kind of lithium ion battery life-span prediction method under random discharge environment based on DGWO-ELM |
Non-Patent Citations (1)
| Title |
|---|
| 徐永海等: "基于核极限学习机的风光容量配置研究", 《电测与仪表》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112100920A (en) * | 2020-09-15 | 2020-12-18 | 东南大学 | Power distribution network three-phase voltage calculation method, device, equipment and storage medium |
| CN112433122A (en) * | 2020-11-23 | 2021-03-02 | 广州橙行智动汽车科技有限公司 | Charging pile available state detection method, device, equipment and storage medium |
| CN115542235A (en) * | 2022-11-07 | 2022-12-30 | 北京志翔科技股份有限公司 | Method, device and equipment for determining metering error of charging gun and storage medium |
| CN117074840A (en) * | 2023-10-16 | 2023-11-17 | 南京思宇电气技术有限公司 | DC charging pile on-line testing system |
| CN117074840B (en) * | 2023-10-16 | 2023-12-19 | 南京思宇电气技术有限公司 | DC charging pile on-line testing system |
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