CN114759558A - Non-invasive online rapid detection method for charging load of electric bicycle - Google Patents
Non-invasive online rapid detection method for charging load of electric bicycle Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/70—Load identification
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Abstract
The invention discloses a non-invasive online rapid detection method for a charging load of an electric bicycle, which comprises the following steps: and sequentially carrying out state conversion removal, down-sampling and SG filtering on the power data of the electric bicycle with the frequency of 1Hz for independent charging, respectively calculating the difference signals of active/reactive power, and constructing a charging load template of the electric bicycle by utilizing a fitting curve of the difference signals in a constant voltage stage. Acquiring active/reactive power data of a user at a home, taking a time window as 6 hours and a step length as 10 minutes, sequentially performing state conversion removal, down-sampling and SG filtering in the window, respectively calculating differential signals of active/reactive power, matching continuous negative subsequences in the active power differential signals with continuous positive subsequences in the reactive power differential signals, calculating the distance between the successfully matched subsequences and a template, and judging that the electric bicycle charging load exists at the position if the distance is smaller than a distance threshold value. The method has wide application prospect in the fields of illegal charging inspection of the electric bicycle and the like.
Description
Technical Field
The invention relates to the field of monitoring of charging loads of electric bicycles, in particular to a non-invasive online rapid detection method for the charging loads of the electric bicycles based on local characteristics.
Background
Under the unified deployment of the strategic target of 'double carbon', clean electric energy substitution becomes an important method for getting rid of the dependence of fossil energy. In the traffic field, the new energy automobile and electric bicycle industry in China also enters the rapid development period. Among them, the social keeping amount of electric bicycles has exceeded 3 hundred million. However, due to the lack of planning and management in the electric bicycle charging places, the weak safety consciousness of residents and other reasons, related fire accidents frequently occur, and huge casualties and property loss are often caused. Therefore, departments such as electric power departments, property departments and the like often need to manually detect illegal charging behaviors of users on site, but the problems of low efficiency, low user adaptability and the like exist. The Non-intrusive Load Monitoring (NILM) technology does not need to intrude into the user, can acquire detailed power utilization information of each electric appliance of the user only by processing and analyzing Load power utilization total data, and can analyze the power utilization behavior of the user according to the detailed power utilization information. Therefore, the NILM technology is applied to the efficient detection of illegal charging of the electric bicycle, so that the practical applicability is very high, and the efficient and convenient monitoring technology further has a wide application prospect in the fields of health state assessment, charging electric quantity inquiry, energy efficiency analysis and the like of the electric bicycle.
At present, the application of the non-intrusive load monitoring technology in residential users mainly focuses on some common household appliances, and the research on the method for detecting the charging load of the electric bicycle is less. And although the most advanced NILM method is able to break down most household appliance loads, the electric bicycle charging load is a continuously variable load, and the identification and breaking down of such loads is still a difficult task. The existing method for identifying the continuously variable load needs a large-scale training data set or high sampling frequency data for extracting transient characteristics, which cannot be met by the charging intelligent electric meter, and also limits large-scale popularization and application of the methods. On the other hand, the electric bicycle charging load has a long running time, and can run in a mixed mode with other electric appliance loads, so that great challenges are brought to recognition and decomposition work. In addition, although a few unsupervised non-intrusive electric bicycle charging load detection methods exist, online detection cannot be achieved.
Disclosure of Invention
In consideration of the defects in the prior art, in order to further realize the rapid discovery of the charging load of the electric bicycle, the invention provides a non-invasive online rapid detection method of the charging load of the electric bicycle based on local characteristics by combining a non-invasive load monitoring technology, and aims to meet the requirements of timely and rapid discovery and positioning of the charging energy of the electric bicycle in an actual power utilization scene. The invention can accurately and quickly realize the online detection of the electric bicycle and has wide application prospect in the fields of illegal charging inspection of the electric bicycle and the like.
In order to solve the above technical problems, the present invention provides a non-intrusive method for rapidly detecting a charging load of an electric bicycle on line, which mainly comprises: constructing a charging load template of the electric bicycle; judging whether the charging load of the electric bicycle exists in the power load of the detected user; and finally, detecting whether the electric load of the detected user has the charging load of the electric bicycle. The method comprises the following specific steps:
1-1) acquiring active power and reactive power data of independent charging of a plurality of electric bicycles with the sampling frequency of 1 Hz;
1-2) data preprocessing: taking a time window for the active power and reactive power data acquired in the step 1-1), and removing load events in the total active power and the total reactive power in the window by adopting a state conversion removal algorithm; reducing the sampling frequency of the active power data and the reactive power data to 1/30Hz, and adopting Savitzky-Golay (SG) filtering to reduce the noise in the power signals after frequency reduction;
1-3) calculating difference of the active power and reactive power data preprocessed in the step 1-2) to obtain an active power difference signal and a reactive power difference signal;
1-4) respectively fitting the active power differential signal and the reactive power differential signal in a constant voltage charging stage into a line segment, and taking the maximum point and the slope of the fitted line segment as characteristic vector parameters, so as to establish an electric bicycle charging load template comprising an active power differential signal template and a reactive power differential signal template;
2-1) acquiring active power data and reactive power data of the user to be detected at the user entrance, and calculating to obtain an active power differential signal and a reactive power differential signal of the user entrance according to the step 1-2) and the step 1-3);
2-2) matching continuous negative subsequences in the active power differential signals and continuous positive subsequences in the reactive power differential signals, which are obtained by calculation in the step 2-1), of the user's house, if the subsequences are successfully matched, executing the step 3, and otherwise, repeatedly executing the step 2;
and 3, step 3: and (3) calculating the distance between the continuous subsequence successfully matched in the step (2) and the single charging load template of the electric bicycle constructed in the step (1) to be L, if the distance L is more than or equal to a preset distance threshold value, returning to the step (2), otherwise, detecting that the charging load of the electric bicycle exists in the electric load of the user.
Further, the non-intrusive method for rapidly detecting the charging load of the electric bicycle on line provided by the invention comprises the following steps:
for step 1-1), the number of electric bicycles is 10.
For step 1-2), the length of the time window is set to 6 hours, and the step size is set to 10 minutes; the method comprises the following steps that a state conversion removal algorithm is adopted to remove load events in total active power and total reactive power in a window, namely, in power data of independent charging of the electric bicycle, a starting event of the electric bicycle is removed; in the power data at the user entrance, the on, running, off events of the electric loads other than the electric bicycle are removed.
For step 2-2), the continuous positive subsequence is a differential signal sequence with a reactive power differential signal larger than 0.06 and more than 8 continuous sampling points in the same state; the continuous negative subsequence is a differential signal sequence with active power differential signals less than-0.1 and more than 8 continuous sampling points in the same state; the successful matching means that the continuous negative active power differential signal subsequence and the continuous positive reactive power differential signal subsequence are overlapped in time.
For step 3, the distance L is an average of a mean of a sum of squares of differences between an active power differential signal template in the electric bicycle charging load template and a successfully matched active power differential continuous subsequence, and a mean of a sum of squares of differences between a reactive power differential signal template in the electric bicycle charging load template and a successfully matched reactive power differential continuous subsequence.
Compared with the prior art, the invention has the beneficial effects that:
the non-invasive load monitoring technical method is applied to the charging load detection of the electric bicycle, the local characteristics of the charging load of the electric bicycle are extracted, the non-invasive online rapid detection method of the charging load of the electric bicycle based on the local characteristics is established, and the online detection of the electric bicycle can be accurately and rapidly realized under the condition of not invading the interior of a user. The method can meet the requirement of timely and fast finding and positioning the charging capacity of the electric bicycle in an actual power utilization scene, and has wide application prospects in the fields of illegal charging inspection of the electric bicycle and the like.
Drawings
FIG. 1 is a flow chart of the on-line rapid detection method of the present invention;
fig. 2(a) is a schematic diagram of model active power filtering during a charging phase of an electric bicycle;
fig. 2(b) is a schematic diagram of reactive power filtering of a model of an electric bicycle charging phase;
FIG. 2(c) is a schematic diagram of the model filtered active power differential signal during the charging phase of the electric bicycle;
FIG. 2(d) is a schematic diagram of the filtered reactive power differential signal of the model of the charging stage of the electric bicycle;
FIG. 3(a) is a schematic diagram of the detected load event detection and SG filtering;
FIG. 3(b) is a schematic diagram of detected state transition removal and SG filtering;
fig. 4(a) is a graph of the charging load slope versus the active power difference for 10 electric bicycles;
fig. 4(b) is a charging load slope-reactive power difference diagram for 10 electric bicycles;
fig. 5(a) is a schematic diagram of the SG filtered active power differential signal and continuous negative subsequences;
fig. 5(b) is a schematic diagram of SG filtered reactive power differential signal and continuous positive subsequence;
FIG. 6(a) shows the real values of the user power and the EBCL power for the No. 1 user for the online detection of the charging load of the electric bicycle at a certain time;
fig. 6(b) is a schematic diagram of active power differential signals and continuous negative subsequences after SG filtering is detected on line by the charging load of the electric bicycle of user No. 1 at a time;
fig. 6(c) is a schematic diagram of the SG filtered reactive power differential signal and the continuous positive subsequence after the charging load of the electric bicycle of the user number 1 at a certain time is detected online.
Detailed Description
The design idea of the non-invasive online rapid detection method for the charging load of the electric bicycle is that in the research process, through the collected active power and reactive power data of the electric bicycle which is independently charged, the electric bicycle is found to have a constant voltage charging stage which is different from other electric appliance loads, and the stage shows the load characteristics of a descending gentle slope of active power and an ascending gentle slope of reactive power, so that the charging load of the electric bicycle is detected by adopting the local characteristics of the constant voltage stage, namely the load characteristics that the difference between the amplitudes of the active power and the reactive power is gradually increased.
The invention will be further described with reference to the following drawings and specific examples, which are not intended to limit the invention in any way.
The method mainly comprises the steps of constructing a charging load template of the electric bicycle by adopting local characteristics of a constant voltage stage, then acquiring active power data and reactive power data of a detected user at the user-in position, and judging whether the charging load of the electric bicycle is suspected to exist in the power load of the detected user according to an active power differential signal and a reactive power differential signal of the user-in position obtained through calculation; and finally, comparing the detected user with the electric bicycle charging load template and calculating to detect whether the electric bicycle charging load exists in the electric load of the detected user. As shown in fig. 1, the specific steps are as follows:
1-1) acquiring active power and reactive power data of independent charging of 10 electric bicycles simultaneously with the sampling frequency of 1 Hz;
1-2) data preprocessing: taking a time window for the data acquired in the step 1-1) of the independent charging of the electric bicycle, and removing load events in the total active power and the total reactive power in the window by adopting a state transition removal algorithm.
The Method adopts a self-Adaptive Two-stage Event Detection Method provided by Luan W, Liu Z, Liu B, et al.an Adaptive Two-stage Load Event Detection Method for nonlinear Load Monitoring. And then removing it from the total amount data as detected using a state transition removal algorithm.
Load events in reactive power are determined from events in active power, and then detected events are removed using a state transition removal algorithm. The purpose of state transition removal is to restore the Electric Bicycle Charging Load (EBCL) gradual slope trend characteristics divided by the state transitions of other electrical loads. When a load event is detected in the power signal, it is removed from the aggregate data using equation (1):
where τ denotes the time index of the signal being analyzed, Z denotes the active and reactive power signals, Δ Z denotes the values of the active and reactive power of the detected event, L tRepresenting the active power signal and the reactive power signal after the state transition is removed.
And (3) down-sampling and filtering, namely, reducing the sampling frequency of the single charging data of the electric bicycle to 1/30Hz, and adopting Savitzky-Golay (SG) filtering to reduce the noise in the down-converted power signal.
In the invention, in order to simultaneously reserve the gentle slope characteristic of the electric bicycle and reduce the interference of other electric appliance loads, the average value is calculated once every 30 sampling points to be used as a new sampling point, namely the sampling frequency is reduced to 1/30 Hz. Because the signal after the state transition is removed still contains the fluctuation in the operation of the electric appliance load, in order to further reduce the fluctuation and smooth the gentle slope characteristic corresponding to the constant voltage charging stage of the electric bicycle, the Savitzky-Golay filtering (SG filtering) smoothing signal is adopted in the invention. SG filtering is a filtering method based on local least square polynomial fitting by adopting a sliding window, and the filtering method can eliminate different frequency noises, simultaneously reserve the peak value and the width of an original signal, and is widely applied to signal denoising with non-Gaussian noise. Compared with filtering methods such as mean filtering, Kalman filtering and the like, SG filtering has better signal shape keeping and denoising performance under the condition of not losing resolution.
Given a locally symmetrical data window n ═ l of length 2m +1-m,l-m+1,...,l0,...,lm-1,lm]，liRepresenting the filtered signal LtIf the active power and the reactive power of the middle sampling point are data, the SG filtered signal is:
wherein p < 2m, denotes the order of a least squares polynomial, akDenotes a coefficient of polynomial l'nIs the active power and reactive power information corresponding to the data window n after SG filteringNumber (n).
In the invention, the length of the time window is set to be 6 hours, and the step length is set to be 10 minutes. The load events in the total active power and the total reactive power in the window are removed by adopting a state conversion removal algorithm, and the starting events of the electric bicycle are removed according to the power data of the electric bicycle for independent charging.
1-3) calculating power data difference, and calculating difference of the electric bicycle independent charging data preprocessed in the step 1-2) to obtain an active power difference signal and a reactive power difference signal of the electric bicycle independently charged.
1-4) constructing a charging load template of the electric bicycle by utilizing the preprocessed single charging power data of the electric bicycle.
The SG filtering makes the transition process from the constant-current charging stage to the constant-voltage charging stage smoother, small fluctuations in the constant-voltage charging stage are also smoothed, and the differential signal is calculated for the processed active power data and reactive power data, as shown in fig. 2(a), 2(b), 2(c), and 2(d), the power differential signal in the constant-voltage charging stage can be fitted into a line segment, and the minimum point and the slope of the fitted line segment are used as the characteristic vector parameters of the template. Respectively fitting the constant voltage sections of the active power data and the reactive power data to establish an active power differential signal template of the charging load of the electric bicycle 
And reactive power differential signal template
Fig. 2(a) shows a model active power filtering schematic diagram of an electric bicycle charging phase; fig. 2(b) shows a model reactive power filtering schematic diagram of the charging phase of the electric bicycle; fig. 2(c) shows the model filtered active power differential signal during the charging phase of the electric bicycle; fig. 2(d) shows the filtered reactive power differential signal of the electric bicycle charging phase model.
And 2, judging whether the charging load of the electric bicycle exists in the power load of the detected user.
2-1) acquiring active power data and reactive power data of the user to be detected at the user entrance, and according to the step 1-2) and the step 1-3), substituting the data in the process into the power data acquired from the user entrance, and after down-sampling and filtering, removing the events of opening, running and closing of the electric appliance loads except the electric bicycle according to the power data at the user entrance. And calculating power data difference to obtain an active power difference signal and a reactive power difference signal at the user home.
2-2) calculating power data difference: matching the continuous negative subsequence in the active power differential signal at the user entrance position obtained by calculation in the step 2-1) with the continuous positive subsequence in the reactive power differential signal.
The invention sets the difference of active power less than-0.1 as negative, the difference of reactive power more than 0.06 as positive, and stipulates more than 8 continuous sampling points as the same state and can be divided into continuous subsequences. The active power descending gentle slope and the reactive power ascending gentle slope in the constant voltage charging stage of the electric bicycle are synchronous in time, so that the continuous negative active power differential signal subsequence and the continuous positive reactive power differential signal subsequence need to be matched, namely the continuous negative active power differential signal subsequence and the continuous positive reactive power differential signal subsequence are overlapped in time and are considered to be matched. If the matching is successful, executing the step 3, otherwise, repeatedly executing the step 2.
And 3, step 3: and matching with the charging load template of the electric bicycle, and determining that the matching is matched with the template if the distance is smaller than a preset distance threshold value by calculating the distance between the matched continuous subsequence and the charging load template of the electric bicycle.
For the active power differential signal continuous negative subsequence and the reactive power differential signal continuous positive subsequence which are synchronized in time (i.e. successfully matched), the distances between the active power differential signal subsequence and the electric bicycle charging load template are respectively calculated, wherein the distances refer to the average value of the square sum of the difference values between the active power differential signal template in the electric bicycle charging load template and the successfully matched active power differential continuous subsequence, and the average value of the square sum of the difference values between the reactive power differential signal template in the electric bicycle charging load template and the successfully matched reactive power differential continuous subsequence, as shown in formula (3):
Wherein x is a charging load template signal of the electric bicycle, y is a successfully matched power difference continuous subsequence, and x1Is the minimum point of the template, y1The maximum point of the continuous subsequence is n, the number of data points from the maximum point to the end point of the subsequence in the continuous subsequence of the differential signal is n, and it is noted that n is required to be controlled to avoid the situation that the maximum point is at the last position in the continuous subsequence and is close to the maximum point of the template>10, Δ S represents the active signal subsequence distance and the active signal subsequence distance. Taking the average value of the active signal subsequence distance and the reactive signal subsequence distance as the distance between the matching subsequence and the template, if the average value is smaller than a heuristic set distance threshold value, considering that the subsequence is matched with the template, and detecting that the charging load of the electric bicycle exists in the electric load of the user; if the value is larger than or equal to the threshold value, the subsequence is not matched with the template, and the constant-voltage charging stage of the electric bicycle does not exist.
Study materials example 1:
fig. 3(a) and 3(b) show the result of load event detection and the result of state transition removal for data at a user entrance at a certain time period, and show the comparison images before and after SG filtering, which shows that the load event detection algorithm can accurately detect events in the total power consumption data.
And constructing an electric bicycle charging load template by using the active power data and the reactive power data of the 10 electric bicycles for individual charging. The 10 electric bicycle charging load model parameters shown in the above table were obtained. Plotting 10 electric bicycle charging loads according to the data in table 1, fig. 4(a) shows a slope-active power difference diagram for 10 electric bicycle charging loads, and fig. 4(b) shows a slope-reactive power diagram for 10 electric bicycle charging loadsAnd (4) a differential graph. So as to see that the model of the lead-acid battery No. 5 is obviously deviated from the rest 9 models, the models are removed, and the average value of the rest 9 models is taken to obtain the EBCL template, namely
Lowest point: -1.2836;
slope ratio: 0.0100;
highest point: 0.5548, respectively;
slope ratio: -0.0054.
Table 110 electric bicycle charging load model parameters
Calculating the differential signal for the active power and reactive power data at the house and matching the continuous negative subsequence in the active power differential signal with the continuous positive subsequence in the reactive power differential signal, as shown in fig. 5(a) and 5(b), it can be seen that the two deepened subsequences near 00:00 are matched. Finally, the distance threshold is set to 0.25, and fig. 6(a), 6(b) and 6(c) show the online detection result of the charging load of the electric bicycle for the user No. 1 at a certain time, wherein fig. 6(a) is the online detection user power and the actual value of the EBCL power of the charging load of the electric bicycle for the user No. 1 at a certain time; fig. 6(b) is a schematic diagram of active power differential signals and continuous negative subsequences of SG filtering for online detection of charging load of the electric bicycle of the user # 1 at a certain time; fig. 6(c) is a schematic diagram of the SG filtered reactive power differential signal and the continuous positive subsequence after the charging load of the electric bicycle of the user number 1 at a certain time is detected online.
Study materials example 2:
meanwhile, the charging load of the electric bicycle is detected on line according to the active power and reactive power data of an entrance of 411 days for 7 users in total, the experimental results in 7 users are shown in the table 2, the condition that the charging load is missed to be detected hardly exists, and meanwhile, the online rapid detection of 20 minutes to 45 minutes after the constant-voltage charging is started is basically realized.
TABLE 2 on-line quick detection result of charging load of electric bicycle
According to the implementation, the charging load of the electric bicycle can be quickly detected according to the summarized charging load template of the electric bicycle under the condition that the charging load template does not invade the interior of a user, and the accuracy is high. The method has great practical applicability in the application of efficient detection of illegal charging of the electric bicycle, can meet the requirements of rapid discovery and positioning of illegal charging behaviors of the electric bicycle in an actual power utilization scene, and has wide application prospects in the fields of illegal charging inspection of the electric bicycle and the like.
Although the present invention has been described in connection with the accompanying drawings, the present invention is not limited to the above-described embodiments, which are intended to be illustrative rather than restrictive, and many modifications may be made by those skilled in the art without departing from the spirit of the present invention as disclosed in the appended claims.
Claims (5)
1. A non-intrusive type electric bicycle charging load online rapid detection method is characterized by comprising the following steps:
step 1, constructing a charging load template of the electric bicycle, comprising the following steps:
1-1) acquiring active power and reactive power data of independent charging of a plurality of electric bicycles with the sampling frequency of 1 Hz;
1-2) data preprocessing: taking a time window for the active power and reactive power data acquired in the step 1-1), and removing load events in the total active power and the total reactive power in the window by adopting a state conversion removal algorithm; reducing the sampling frequency of the active power data and the reactive power data to 1/30Hz, and adopting Savitzky-Golay (SG) filtering to reduce the noise in the power signals after frequency reduction;
1-3) calculating difference of the active power and reactive power data preprocessed in the step 1-2) to obtain an active power difference signal and a reactive power difference signal;
1-4) respectively fitting the active power differential signal and the reactive power differential signal in a constant voltage charging stage into a line segment, and taking the maximum point and the slope of the fitted line segment as characteristic vector parameters, thereby establishing an electric bicycle charging load template comprising an active power differential signal template and a reactive power differential signal template;
Step 2, judging whether the charging load of the electric bicycle exists in the power load of the detected user:
2-1) acquiring active power data and reactive power data of the user to be detected at the user position, and calculating to obtain an active power differential signal and a reactive power differential signal at the user position according to the step 1-2) and the step 1-3);
2-2) matching continuous negative subsequences in the active power differential signals and continuous positive subsequences in the reactive power differential signals, which are obtained by calculation in the step 2-1), of the user's house, if the subsequences are successfully matched, executing the step 3, and otherwise, repeatedly executing the step 2;
and 3, step 3: and (3) calculating the distance between the continuous subsequence successfully matched in the step (2) and the single charging load template of the electric bicycle constructed in the step (1) to be L, if the distance L is more than or equal to a preset distance threshold value, returning to the step (2), otherwise, detecting that the charging load of the electric bicycle exists in the electric load of the user.
2. The method for the on-line rapid detection of the charging load of the non-invasive electric bicycle according to claim 1, wherein for step 1-1), the number of the electric bicycles is 10.
3. The method for the on-line fast detection of the charging load of the non-invasive electric bicycle according to claim 1, wherein for the steps 1-2), the length of the time window is set to 6 hours, and the step length is set to 10 minutes;
The method comprises the following steps that a load event in total active power and total reactive power in a window is removed by adopting a state conversion removal algorithm, namely, a starting event of the electric bicycle is removed in power data of independent charging of the electric bicycle; in the power data at the user entry, the on, running, off events of the electrical loads other than the electric bicycle are removed.
4. The method for rapidly detecting the charging load of the non-invasive electric bicycle on line according to claim 1, wherein for step 2-2), the continuous positive subsequence is a differential signal sequence in which the reactive power differential signal is greater than 0.06 and more than 8 continuous sampling points are in the same state; the continuous negative subsequence is a differential signal sequence with active power differential signals smaller than-0.1 and more than 8 continuous sampling points in the same state; the successful matching means that the continuous negative active power differential signal subsequence and the continuous positive reactive power differential signal subsequence overlap in time.
5. The method for non-invasive online rapid detection of charging load of electric bicycle according to claim 1, wherein for step 3, the distance L is an average of a mean of a sum of squares of differences between the active power differential signal template in the charging load template of electric bicycle and the successfully matched active power differential continuous subsequence, and a mean of a sum of squares of differences between the reactive power differential signal template in the charging load template of electric bicycle and the successfully matched reactive power differential continuous subsequence.
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| CN116526496B (en) * | 2023-06-16 | 2023-09-08 | 国网山西省电力公司晋城供电公司 | Novel auxiliary decision-making method for power system load control |
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