CN117370873A - Non-invasive load extraction method for charging load of residential electric automobile - Google Patents
Non-invasive load extraction method for charging load of residential electric automobile Download PDFInfo
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- 238000000605 extraction Methods 0.000 title claims abstract description 33
- 238000001914 filtration Methods 0.000 claims abstract description 42
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000004378 air conditioning Methods 0.000 claims abstract description 15
- 239000000758 substrate Substances 0.000 claims abstract description 8
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 6
- 230000001186 cumulative effect Effects 0.000 claims description 15
- 239000012634 fragment Substances 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 3
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- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
Abstract
The invention discloses a non-invasive load extraction method for charging load of a residential electric automobile, which comprises the following steps: collecting residential user ammeter data, counting charging power data of the electric automobile to obtain an aggregate signal, and subtracting a substrate load in the aggregate signal to obtain an aggregate signal minus the substrate load; setting a filtering threshold value, and performing low-amplitude filtering on the aggregate signal subtracted with the base load to obtain a low-amplitude filtered signal; filtering short-time peak power segments in the low-amplitude filtered signals to obtain the number of residual power segments and charging starting time of each residual power segment, wherein the number of residual power segments and the charging starting time of each residual power segment are used as signals for filtering short-time peak power segments; removing residual noise in the signals of the short-time peak power section to obtain signals after the residual noise is removed; classifying the signals with the residual noise removed to obtain three types of power segments; the three types of power segments are respectively subjected to load decomposition to extract the charging load waveform of the residential electric automobile, and the method can eliminate the interference of air conditioning signals.
Description
Technical Field
The invention relates to the technical field of power supply monitoring, in particular to a non-invasive load extraction method for charging loads of residential electric vehicles.
Background
Under the dual driving of policy and market, the permeability of Electric Vehicles (EV) in the automotive market is increasing year by year. Meanwhile, the charging and discharging behaviors of the large-scale electric automobile seriously influence the safe and stable operation of the power grid, wherein the influence of a family user is most prominent, and the severity of the influence is determined by factors such as the charging time, the charging duration and the charging frequency of the family electric automobile. How to accurately identify the charging behavior of the electric automobile is a key for realizing safe and stable operation of a power grid by coping with disordered charging impact. Conventional invasive and semi-invasive load monitoring methods are expensive to manufacture, difficult to maintain and not acceptable to users, and therefore, extracting the charging load pattern of residential electric vehicle users using Non-invasive load monitoring methods (Non-Intrusive Load Monitoring, NILM) is currently an acceptable and effective method.
In the existing non-invasive load extraction method of the electric automobile, since the air-conditioning load waveform is similar to the electric automobile load waveform, how to eliminate the interference of the air-conditioning signal is a great difficulty; the electric equipment in the families of different residential users has larger difference, and the charging load of the electric automobile has larger difference, for example, the electric automobiles of different users have different charging amplitude, different charging duration and different charging times and time, so that the method is possibly suitable for some residences, has poor decomposition effect on other residences and has no universal applicability.
Disclosure of Invention
Aiming at the defects in the prior art, the non-invasive load extraction method for the charging load of the residential electric automobile solves the problems that the interference of an air-conditioning signal is difficult to eliminate and the universal applicability is not achieved in the existing method.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a non-invasive load extraction method facing to charging load of a residential electric automobile comprises the following steps:
s1, acquiring intelligent ammeter data of a residential user, and counting charging power data of an electric automobile to obtain an aggregate signalxSubtracting the substrate load from the aggregate signal to obtain an aggregate signal minus the substrate load;
s2, setting a filtering threshold valueT low Low-amplitude filtering is carried out on the aggregate signal subtracted with the base load, so as to obtain a low-amplitude filtered signal;
s3, filtering short-time peak power segments in the low-amplitude filtered signals to obtain the number of residual power segments and charging starting time of each residual power segment, and taking the number of residual power segments and charging starting time of each residual power segment as signals for filtering short-time peak power segments;
s4, removing residual noise in the signals with short-time peak power sections filtered, and obtaining signals with the residual noise removed;
s5, classifying the signals with the residual noise removed to obtain three types of power segments;
and S6, respectively carrying out load decomposition on the three types of power segments, and extracting the charging load waveform of the residential electric automobile.
Further: in the step S1, signals are aggregated fromxThe expression of subtracting the base load is:
wherein,x min to aggregate signalsxThe minimum value of the non-zero element in (c),x t to pair(s)xMiddle (f)tThe power values of the individual observations are used,x t ′to pair(s)xMiddle (f)tThe power value of the base load is subtracted from each observed value.
Further: in the step S2, the expression for obtaining the low-amplitude filtered signal is:
wherein,x t ′′is filtered with low amplitudetPower value at time.
Further: the step S3 comprises the following sub-steps:
s31, setting a duration threshold, finding out all power fragments with duration less than the duration threshold in the low-amplitude filtered signal, and marking the power fragments as filtering;
s32, traversing short-time power segments with duration lower than a duration threshold in the low-amplitude filtered signals, and marking the power segments meeting all filtering conditions as filtering;
and S33, deleting all the power segments marked as filtered to obtain the number of the residual power segments and the charging start time of each residual power segment, and using the number of the residual power segments and the charging start time as signals for filtering short-time peak power segments.
Further: the filtering conditions in step S32 include:
1）D i+1 <（1+η）D i ；
2）Gap≤3D i ；
wherein,D i for the duration of the current power segment,D i+1 for the duration of the next power segment adjacent to the current power segment,ηin order to adjust the parameters of the device,Gapis the interval time between the current power segment and the next power segment adjacent to the current power segment.
Further: the method for removing residual noise in the signals for filtering the short-time peak power section in the step S4 comprises the following steps: before the middle section of the signal for filtering short-time peak power section is takenNaPost-point and segmentNbTaking the average value of the minimum value of the points as the amplitude value of the residual noise, and filtering the residual noise;
wherein,NaandNball represent points in time.
Further: the three types of power segments in the step S5 are respectively as follows:
type 1: the electric vehicle charging waveform is overlapped by the dryer or the oven, and the duration of the electric vehicle charging waveform is consistent with the electric vehicle charging duration;
type 2: belongs to the waveform of electric automobile charging or air conditioning, or the waveform of overlapping electric automobile/air conditioner with other non-electric automobile/air conditioning equipment;
type 3: belonging to the waveform of overlapping electric automobile and air conditioner load, the electric automobile waveform is at the bottom or top of overlapping.
The power segments are further classified according to the following:
f(y)={S(t)>y}，0≤y≤max(S(t))
wherein,f(.) is a cumulative function,yfor any value from 0 to the maximum amplitude,S(t)>yindicating that the amplitude in the power segment is greater thanyIs a set of all sampling points;
the method for classifying the signals after removing the residual noise comprises the following steps: usingMATLABSelf-containedfindpeaksThe gradient of the function finding power segment cumulative function has a plurality of peaks;
wherein the power segment without peak in the gradient of the power segment cumulative function is of type 1;
the power segment with one peak in the gradient of the power segment cumulative function is of type 2;
the power segment with two peaks in the gradient of the power segment cumulative function is type 3.
Further: in the step S6, for the type 1 power segment, calculating an effective height and an effective width of the power segment, taking the effective height as a charging power amplitude of the residential electric vehicle, taking the effective width as a charging duration of the residential electric vehicle, and combining the charging start time of the power segment to obtain a charging load waveform of the residential electric vehicle, thereby completing extraction of the charging load waveform of the residential electric vehicle;
for the type 2 power segments, calculating the effective height and the effective width of the power segments, taking the effective height as the charging power amplitude of the residential electric vehicle, taking the effective width as the charging duration of the residential electric vehicle, deleting the power segments with the effective width more than 250min and the power segments with the effective height less than 3kW, combining the charging starting time of the power segments to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle;
for a power segment of type 3,setting a threshold valueT h Power is lowered below a threshold valueT h Obtaining a top sub-power segment of the power segment, and judging whether the top sub-power segment of the power segment is an air-conditioning power segment:
if not, calculating the effective width and the effective height of the charge load waveform of the residential electric vehicle, taking the effective height as the charge power amplitude of the residential electric vehicle, taking the effective width as the charge duration of the residential electric vehicle, and combining the power segment charge starting time to finish the extraction of the charge load waveform of the residential electric vehicle;
if yes, deleting the electric vehicle to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle.
Further: the method for judging whether the type 3 power segment is an air conditioner power segment comprises the following steps: judging whether the duration of the power segment exceeds 250min or whether the waveform of the power segment has periodicity;
if yes, the power segment is an air conditioner power segment;
if not, the power segment is not the air conditioner power segment.
Drawings
Fig. 1 is a flow chart of a non-invasive load extraction method for charging load of an electric vehicle according to the present invention.
Fig. 2 is an illustration of the original aggregate power signal in an embodiment of the present invention.
FIG. 3 is a graph showing the power signal after removal of the base load in an embodiment of the present invention.
Fig. 4 is a low-amplitude filtered signal in an embodiment of the invention.
FIG. 5 is a diagram illustrating filtering short-term peak power segments according to an embodiment of the present invention.
Fig. 6 is a comparison diagram of an actual charging waveform of an electric vehicle and an electric vehicle charging waveform decomposed by the method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, in one embodiment of the present invention, a non-invasive load extraction method for charging loads of residential electric vehicles includes the steps of:
s1, acquiring intelligent ammeter data of a residential user, and counting charging power data of an electric automobile to obtain an aggregate signalxSubtracting the substrate load from the aggregate signal to obtain an aggregate signal minus the substrate load;
in the step S1, signals are aggregated fromxThe expression of subtracting the base load is:
wherein,x min to aggregate signalsxThe minimum value of the non-zero element in (c),x t to pair(s)xMiddle (f)tThe power values of the individual observations are used,x t ′to pair(s)xMiddle (f)tSubtracting the power value of the base load from each observed value;
s2, setting a filtering threshold valueT low Low-amplitude filtering is carried out on the aggregate signal subtracted with the base load, so as to obtain a low-amplitude filtered signal;
in the step S2, the expression for obtaining the low-amplitude filtered signal is:
wherein,x t ′′is filtered with low amplitudetA power value at the moment;
s3, filtering short-time peak power segments in the low-amplitude filtered signals to obtain the number of residual power segments and charging starting time of each residual power segment, and taking the number of residual power segments and charging starting time of each residual power segment as signals for filtering short-time peak power segments;
the step S3 comprises the following sub-steps:
s31, setting a duration threshold, finding out all power fragments with duration less than the duration threshold in the low-amplitude filtered signal, and marking the power fragments as filtering;
s32, traversing short-time power segments with duration lower than a duration threshold in the low-amplitude filtered signals, and marking the power segments meeting all filtering conditions as filtering;
the filtering conditions in step S32 include:
1）D i+1 <（1+η）D i ；
2）Gap≤3D i ；
wherein,D i for the duration of the current power segment,D i+1 for the duration of the next power segment adjacent to the current power segment,ηin order to adjust the parameters of the device,Gapthe interval time between the current power segment and the next power segment adjacent to the current power segment is set;
s33, deleting all the power segments marked as filtered to obtain the number of the residual power segments and the charging starting time of each residual power segment, and using the number of the residual power segments and the charging starting time of each residual power segment as signals for filtering short-time peak power segments;
s4, removing residual noise in the signals with short-time peak power sections filtered, and obtaining signals with the residual noise removed;
the method for removing residual noise in the signals for filtering the short-time peak power section in the step S4 comprises the following steps: before the middle section of the signal for filtering short-time peak power section is takenNaPost-point and segmentNbTaking the average value of the minimum value of the points as the amplitude value of the residual noise, and filtering the residual noise;
wherein,NaandNball represent time points;
s5, classifying the signals with the residual noise removed to obtain three types of power segments;
the three types of power segments in the step S5 are respectively as follows:
type 1: the electric vehicle charging waveform is overlapped by the dryer or the oven, and the duration of the electric vehicle charging waveform is consistent with the electric vehicle charging duration;
type 2: belongs to the waveform of electric automobile charging or air conditioning, or the waveform of overlapping electric automobile/air conditioner with other non-electric automobile/air conditioning equipment;
type 3: the waveform belongs to the waveform of overlapping electric vehicles and air conditioning loads, and the waveform of the electric vehicles is at the bottom or the top of the overlapping;
the power segment classification is based on the following:
f(y)={S(t)>y}，0≤y≤max(S(t))
wherein,f(.) is a cumulative function,yfor any value from 0 to the maximum amplitude,S(t)>yindicating that the amplitude in the power segment is greater thanyIs a set of all sampling points;
the method for classifying the signals after removing the residual noise comprises the following steps: usingMATLABSelf-containedfindpeaksThe gradient of the function finding power segment cumulative function has a plurality of peaks;
wherein the power segment without peak in the gradient of the power segment cumulative function is of type 1;
the power segment with one peak in the gradient of the power segment cumulative function is of type 2;
the power segment with two peaks in the gradient of the power segment cumulative function is of type 3;
s6, respectively carrying out load decomposition on the three types of power segments, and extracting a charging load waveform of the residential electric automobile;
in the step S6, for the type 1 power segment, calculating an effective height and an effective width of the power segment, taking the effective height as a charging power amplitude of the residential electric vehicle, taking the effective width as a charging duration of the residential electric vehicle, and combining the charging start time of the power segment to obtain a charging load waveform of the residential electric vehicle, thereby completing extraction of the charging load waveform of the residential electric vehicle;
for the type 2 power segments, calculating the effective height and the effective width of the power segments, taking the effective height as the charging power amplitude of the residential electric vehicle, taking the effective width as the charging duration of the residential electric vehicle, deleting the power segments with the effective width more than 250min and the power segments with the effective height less than 3kW, combining the charging starting time of the power segments to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle;
for a type 3 power segment, a threshold is setT h Power is lowered below a threshold valueT h Obtaining a top sub-power segment of the power segment, and judging whether the top sub-power segment of the power segment is an air-conditioning power segment:
the method for judging whether the type 3 power segment is an air conditioner power segment comprises the following steps: judging whether the duration of the power segment exceeds 250min or whether the waveform of the power segment has periodicity;
if yes, the power segment is an air conditioner power segment;
if not, the power segment is not the air conditioner power segment;
if not, calculating the effective width and the effective height of the charge load waveform of the residential electric vehicle, taking the effective height as the charge power amplitude of the residential electric vehicle, taking the effective width as the charge duration of the residential electric vehicle, and combining the power segment charge starting time to finish the extraction of the charge load waveform of the residential electric vehicle;
if yes, deleting the electric vehicle to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle.
In order to verify the effectiveness of the method proposed by the invention, the method is based on a real household electricity data setPecanStreetAnd (5) performing verification. Firstly, selecting one day of energy consumption data of a family user to explain the decomposition process of the method.
As shown in FIG. 2, the primary aggregate power signal, base load, is shown in this embodimentx min The power signal after removing the base load is shown in fig. 3, and the low-amplitude filtered amplitude T is taken low =2500W, the low-amplitude filtered signal is shown in fig. 4; locating the position of each power segment, and measuringCalculating duration, and obtaining power segment position information as shown in table 1:
TABLE 1 Power segment position information
The signals for filtering the short-time peak power section are shown in fig. 5, the rest power sections are all of type 1 according to the method of dividing the power section into types, the effective width and the effective height of the rest power sections are calculated respectively, and the actual charging waveform and the decomposed charging waveform of the electric automobile are shown in fig. 6; fig. 6 (a) shows an actual charging waveform of the electric vehicle, and fig. 6 (b) shows an electric vehicle charging waveform extracted in this embodiment.
By usingAcc，RMSE，F 1 The three metrics measure the performance of the method and are defined as follows:
Accthe method comprises the following steps:
RMSEis root mean square error:
f1 is the harmonic mean of accuracy and recall:
wherein:Nas a total number of days,iis the firstiIn the days, the total weight of the product,X true is the actual power of the electric automobile,X est estimating a power value for the method;P RE andR EC the performance for the characterization method to extract the correct charge event,F P a number of samples representing a false extraction as a charging event;T P representing the number of samples correctly extracted as a charging event;F N a number of samples representing a false extraction as a non-charging event;F 1 the closer the value of (2)1, the better the performance of the process.
By decomposing the electricity usage data for the user at 2019, 7, 23 days, the following results can be obtained:
Acc=95.9%，RMSE=0.251，F 1 =0.86。
in the description of the present invention, it should be understood that the terms "center," "thickness," "upper," "lower," "horizontal," "top," "bottom," "inner," "outer," "radial," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be configured and operated in a particular orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be interpreted as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defined as "first," "second," "third," or the like, may explicitly or implicitly include one or more such feature.
Claims (10)
1. The non-invasive load extraction method for the charging load of the residential electric automobile is characterized by comprising the following steps of:
s1, acquiring intelligent ammeter data of a residential user, and counting charging power data of an electric automobile to obtain an aggregate signalxSubtracting the substrate load from the aggregate signal to obtain an aggregate signal minus the substrate load;
s2, setting a filtering threshold valueT low Low-amplitude filtering is carried out on the aggregate signal subtracted with the base load, so as to obtain a low-amplitude filtered signal;
s3, filtering short-time peak power segments in the low-amplitude filtered signals to obtain the number of residual power segments and charging starting time of each residual power segment, and taking the number of residual power segments and charging starting time of each residual power segment as signals for filtering short-time peak power segments;
s4, removing residual noise in the signals with short-time peak power sections filtered, and obtaining signals with the residual noise removed;
s5, classifying the signals with the residual noise removed to obtain three types of power segments;
and S6, respectively carrying out load decomposition on the three types of power segments, and extracting the charging load waveform of the residential electric automobile.
2. The non-invasive load extraction method for charging load of residential electric vehicle according to claim 1, wherein in step S1, the signals are aggregated fromxThe expression of subtracting the base load is:
wherein,x min to aggregate signalsxThe minimum value of the non-zero element in (c),x t to pair(s)xMiddle (f)tThe power values of the individual observations are used,x t ′to pair(s)xMiddle (f)tThe power value of the base load is subtracted from each observed value.
3. The non-invasive load extraction method for charging load of residential electric vehicle according to claim 2, wherein in step S2, the expression for obtaining the low-amplitude filtered signal is:
wherein,x t ′′is filtered with low amplitudetPower value at time.
4. The non-invasive load extraction method for residential electric vehicle charging load according to claim 3, wherein said step S3 comprises the following sub-steps:
s31, setting a duration threshold, finding out all power fragments with duration less than the duration threshold in the low-amplitude filtered signal, and marking the power fragments as filtering;
s32, traversing short-time power segments with duration lower than a duration threshold in the low-amplitude filtered signals, and marking the power segments meeting all filtering conditions as filtering;
and S33, deleting all the power segments marked as filtered to obtain the number of the residual power segments and the charging start time of each residual power segment, and using the number of the residual power segments and the charging start time as signals for filtering short-time peak power segments.
5. The non-invasive load extraction method for charging load of residential electric vehicle according to claim 4, wherein the filtering condition in step S32 comprises:
1）D i+1 <（1+η）D i ；
2）Gap≤3D i ；
wherein,D i for the duration of the current power segment,D i+1 for the duration of the next power segment adjacent to the current power segment,ηin order to adjust the parameters of the device,Gapis the interval time between the current power segment and the next power segment adjacent to the current power segment.
6. The non-invasive load extraction method for residential electric vehicle charging load according to claim 5, wherein the method for removing residual noise in the signal for filtering short-time peak power section in step S4 is as follows: before the middle section of the signal for filtering short-time peak power section is takenNaPost-point and segmentNbTaking the average value of the minimum value of the points as the amplitude value of the residual noise, and filtering the residual noise;
wherein,NaandNball represent points in time.
7. The non-invasive load extraction method for charging load of residential electric vehicle according to claim 6, wherein the three types of power segments in step S5 are respectively:
type 1: the electric vehicle charging waveform is overlapped by the dryer or the oven, and the duration of the electric vehicle charging waveform is consistent with the electric vehicle charging duration;
type 2: belongs to the waveform of electric automobile charging or air conditioning, or the waveform of overlapping electric automobile/air conditioner with other non-electric automobile/air conditioning equipment;
type 3: belonging to the waveform of overlapping electric automobile and air conditioner load, the electric automobile waveform is at the bottom or top of overlapping.
8. The non-intrusive load extraction method for residential electric vehicle charging loads according to claim 7, wherein the power segment classification is based on the following:
f(y)={S(t)>y}，0≤y≤max(S(t))
wherein,f(.) is a cumulative function,yfor any value from 0 to the maximum amplitude,S(t)>yindicating that the amplitude in the power segment is greater thanyIs a set of all sampling points;
the method for classifying the signals after removing the residual noise comprises the following steps: usingMATLABSelf-containedfindpeaksThe gradient of the function finding power segment cumulative function has a plurality of peaks;
wherein the power segment without peak in the gradient of the power segment cumulative function is of type 1;
the power segment with one peak in the gradient of the power segment cumulative function is of type 2;
the power segment with two peaks in the gradient of the power segment cumulative function is type 3.
9. The non-invasive load extraction method for charging load of residential electric vehicle according to claim 8, wherein in step S6, for the type 1 power segment, calculating the effective height and the effective width of the power segment, using the effective height as the charging power amplitude of the residential electric vehicle, using the effective width as the charging duration of the residential electric vehicle, combining the charging start time of the power segment to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle;
for the type 2 power segments, calculating the effective height and the effective width of the power segments, taking the effective height as the charging power amplitude of the residential electric vehicle, taking the effective width as the charging duration of the residential electric vehicle, deleting the power segments with the effective width more than 250min and the power segments with the effective height less than 3kW, combining the charging starting time of the power segments to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle;
for a type 3 power segment, a threshold is setT h Power is lowered below a threshold valueT h Obtaining a top sub-power segment of the power segment, and judging whether the top sub-power segment of the power segment is an air-conditioning power segment:
if not, calculating the effective width and the effective height of the charge load waveform of the residential electric vehicle, taking the effective height as the charge power amplitude of the residential electric vehicle, taking the effective width as the charge duration of the residential electric vehicle, and combining the power segment charge starting time to finish the extraction of the charge load waveform of the residential electric vehicle;
if yes, deleting the electric vehicle to obtain the charging load waveform of the residential electric vehicle, and completing the extraction of the charging load waveform of the residential electric vehicle.
10. The non-intrusive load extraction method for charging loads of residential electric vehicles according to claim 9, wherein the method for judging whether the type 3 power section is an air conditioning power section is as follows: judging whether the duration of the power segment exceeds 250min or whether the waveform of the power segment has periodicity;
if yes, the power segment is an air conditioner power segment;
if not, the power segment is not the air conditioner power segment.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105759113A (en) * | 2016-02-29 | 2016-07-13 | 北京工业大学 | Non-intrusive load monitoring and decomposition method for electric vehicle charging |
| KR101870250B1 (en) * | 2017-03-30 | 2018-06-25 | 한국에너지기술연구원 | Nonintrusive appliance load monitoring device and method |
| US20210196139A1 (en) * | 2019-12-31 | 2021-07-01 | Biosense Webster (Israel) Ltd. | Methods and systems for estimation of residual ecg noise level and adaptive noise threshold |
| CN113928158A (en) * | 2021-08-31 | 2022-01-14 | 天津大学 | Non-invasive electric bicycle monitoring method and system based on model self-learning |
| CN114123185A (en) * | 2021-11-25 | 2022-03-01 | 天津大学 | ICA-R-based non-invasive electric vehicle charging load identification method |
| CN116933175A (en) * | 2023-07-05 | 2023-10-24 | 国网浙江省电力有限公司 | Electric automobile charging load prediction method and device |
-
2023
- 2023-12-08 CN CN202311676028.2A patent/CN117370873A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105759113A (en) * | 2016-02-29 | 2016-07-13 | 北京工业大学 | Non-intrusive load monitoring and decomposition method for electric vehicle charging |
| KR101870250B1 (en) * | 2017-03-30 | 2018-06-25 | 한국에너지기술연구원 | Nonintrusive appliance load monitoring device and method |
| US20210196139A1 (en) * | 2019-12-31 | 2021-07-01 | Biosense Webster (Israel) Ltd. | Methods and systems for estimation of residual ecg noise level and adaptive noise threshold |
| CN113928158A (en) * | 2021-08-31 | 2022-01-14 | 天津大学 | Non-invasive electric bicycle monitoring method and system based on model self-learning |
| CN114123185A (en) * | 2021-11-25 | 2022-03-01 | 天津大学 | ICA-R-based non-invasive electric vehicle charging load identification method |
| CN116933175A (en) * | 2023-07-05 | 2023-10-24 | 国网浙江省电力有限公司 | Electric automobile charging load prediction method and device |
Non-Patent Citations (6)
| Title |
|---|
| YONGJUN ZHOU 等: "An Event-Based Two-Stage Non-intrusive Load Monitoring Method Involved Multi-Dimensional Features", 《CSEE JOURNAL OF POWER AND ENERGY SYSTEMS》, vol. 9, no. 3, pages 1119 - 1128 * |
| 余登武: "基于深度学习的非侵入式负荷监测与分解研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, no. 05, pages 042 - 851 * |
| 周润 等: "基于智能电表集总数据的家庭电动汽车充电行为非侵入式辨识与负荷预测", 《电网技术》, vol. 46, no. 5, pages 1897 - 1906 * |
| 张宝 等: "Savitzky-Golay滤波与局部均值分解相结合的滚动轴承故障诊断方法", 《机械设计与制造》, no. 03, pages 192 - 196 * |
| 梁海峰 等: "智能电网下基于负荷识别的居民电动汽车需求响应特性建模方法研究", 《现代电力》, no. 05, pages 1 - 9 * |
| 汪益川 等: "基于时频分析的噪声去除", 《后勤工程学院学报》, no. 01, pages 49 - 53 * |
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