CN113408481B - Multi-category typical load characteristic analysis and extraction method - Google Patents
Multi-category typical load characteristic analysis and extraction method Download PDFInfo
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Abstract
A multi-category typical load characteristic analysis and extraction method comprises the following steps: collecting multidimensional electricity utilization data of the load side of the electric energy meter; calculating the active power of the load by using the multidimensional electricity consumption data; monitoring the occurrence time of a load action event based on the change condition of the active power; respectively extracting multidimensional electricity data in t periods forwards and backwards by taking the occurrence time of a load action event as a reference; according to the multidimensional electricity utilization data, analyzing and calculating to obtain steady-state characteristic data and transient-state characteristic data of the load; and constructing a time sequence sliding window, and extracting non-electrical characteristic data of the load which is matched with the steady-state characteristic data and the transient characteristic data in a joint way. The invention overcomes the defects of load detection and identification by single characteristic through extracting the steady-state characteristic, transient characteristic and non-electrical characteristic of household load and jointly matching multiple characteristics, and has the advantages of high identification efficiency and small error in the application process.
Description
Technical Field
The invention relates to the technical field of smart grids, in particular to a multi-category typical load characteristic analysis and extraction method.
Background
Along with the maturity of next generation electric energy meter technology, non-intrusive electricity consumption load identification becomes one of the necessary functions of thing allies oneself with the ammeter, and domestic mainstream ammeter producer, electricity consumption new technology research enterprise begin to input a large amount of manpower and materials to carry out preliminary study to non-intrusive load identification technology, and these researches rely on electrical apparatus load data sample storehouse to form an objective external market to resident load data acquisition device demand.
In invasive household load identification technology, the characteristics of the load are the basis of analysis of the technology. In general, the characteristics of household loads are classified into two categories, steady state and transient. The method comprises the steps of extracting characteristic quantities which can be extracted when equipment is in a stable running state, extracting non-electrical characteristics which are lack of characteristic information which is displayed when equipment is switched on and off, and the problems that the existing steady state and transient state characteristics are low in identification efficiency, large in error, poor in expansibility and the like are displayed in an application process. In the prior art, a single load characteristic is often used as a main basis for load detection and identification, so that the accuracy and feasibility of load detection are low, and the load detection cannot be used as a data support of a smart grid.
In summary, load characteristics are required to be extracted in an omnibearing way, and multiple types of load characteristics are effectively matched, so that accurate detection and identification of loads are realized.
Disclosure of Invention
In order to solve the defects existing in the prior art, the invention aims to provide a multi-class typical load characteristic analysis and extraction method, which realizes the joint matching of multi-class load characteristics by extracting the steady-state characteristics, the transient characteristics and the non-electrical characteristics of the load and overcomes the defect that only a single load characteristic is used for load detection and identification.
The invention adopts the following technical scheme.
The multi-category typical load characteristic analysis and extraction method comprises the following steps:
Step 1, collecting multidimensional electricity utilization data of a load side of an electric energy meter;
Step 2, calculating the active power of the load by utilizing the multidimensional electricity consumption data; monitoring the occurrence time of a load action event based on the change condition of the active power;
Step 3, taking the occurrence time of the load action event as a reference, respectively extracting multidimensional electricity data in t periods forwards and backwards;
step 4, analyzing and calculating to obtain steady-state characteristic data and transient-state characteristic data of the load according to the multidimensional electricity consumption data;
and 5, constructing a time sequence sliding window, and extracting non-electrical characteristic data of the load which is matched with the steady-state characteristic data and the transient characteristic data in a joint way.
Preferably, in step 1, the multi-dimensional electricity consumption data includes: steady state active power, steady state reactive power, steady state harmonic current, and transient current.
Preferably, step 2 comprises:
Step 2.1, calculating active power P by adopting Fourier decomposition, wherein the following relation is satisfied:
Wherein U k is a voltage effective value, I k is a current effective value, k is a harmonic order, and k=0, 1, 2; phi k is the power factor angle corresponding to each harmonic frequency;
and 2.2, monitoring a change value delta P of the active power in the current period relative to the previous period, and when the absolute value of the change value delta P is larger than a preset threshold value, determining that a load action event occurs in the current period, and recording the occurrence time of the load action event.
Preferably, in step 3, the number t of periods is not less than 6.
Preferably, in step 4, the steady state characteristic data includes: active power, reactive power, current waveform characteristics, V-I track characteristics, harmonic content and harmonic combination sequencing.
Preferably, the reactive power Q is calculated using fourier decomposition, satisfying the following relation:
Wherein U k is a voltage effective value, I k is a current effective value, k is a harmonic order, and k=0, 1, 2; phi k is the power factor angle corresponding to each harmonic order.
Preferably, the current waveform characteristics include: root mean square I rms, amplitude I P, crest factor I cf and total harmonic distortion THD;
Wherein, root mean square I rms satisfies the following relation:
Wherein m is a counting index, N is the number of sampling points, and i m is the instantaneous current of the sampling points;
Wherein, the amplitude I P satisfies the following relation:
IP＝max(im)0≤m≤N
Wherein, the crest factor I cf satisfies the following relation:
Wherein, the total harmonic distortion THD satisfies the following relation:
Where k is the harmonic order, I k is the kth harmonic current, and I 1 is the fundamental current.
Preferably, the V-I trajectory feature comprises: the number of curve crossing points, the slope of the curve center line and the curve closure area.
Preferably, a harmonic analysis algorithm based on fast Fourier transform redesigns harmonic component data into multiple groups to be combined and ordered, so that harmonic combination ordering is obtained; the harmonic combination ordering includes direct current components, low odd harmonics, medium odd harmonics, high odd harmonics, low even harmonics, medium even harmonics and high even harmonics.
Preferably, in step 4, the transient characteristic data includes: transient duration, transient current step height and strike height, and strike coefficient.
Wherein the transient duration Δt satisfies the following relationship:
ΔT＝Tet-Tst
wherein, transient current step height Δi steady satisfies the following relation:
ΔIsteady＝Iet-Ist
wherein the impact height Δi impulse satisfies the following relation:
ΔIimpulse＝Imax-Ist
wherein the impact coefficient V satisfies the following relation:
wherein, T et is the transient start time, T st is the transient end time, I et is the current value at the transient start time, I st is the current value at the transient end time, and I max is the maximum current value in the transient phase.
Preferably, the non-electrical characteristic data comprises: time characteristics of intermittent operation of load, seasonal characteristics of intermittent operation of load, and related characteristics of intermittent operation of load.
Preferably, in step 5, extracting a time characteristic of the intermittent operation of the load includes:
Step 5.1, constructing sliding windows in unit time, wherein each small window comprises n discrete power points;
Step 5.2, monitoring all load action events in the sliding window; wherein the load action event comprises: a load throw-in event and a load cut-out event;
Step 5.3, calculating the number of intermittent load operation times in unit time;
step 5.4, calculating the duration of intermittent load operation in unit time;
and 5.5, sliding n discrete power points to form a new sliding window, and repeating the step 5.2.
Further, in step 5.3, the number of intermittent load operations per unit time is the sum of the number of load input events and load removal events in the sliding window divided by 2.
Further, in step 5.4, the duration of the intermittent load operation in unit time is the sum of the time intervals of all load shedding events and the next load throwing event.
Compared with the prior art, the method has the advantages that the defects of load detection and identification by single characteristics are overcome through extracting the steady-state characteristics, the transient characteristics and the non-electrical characteristics of the household load and carrying out multi-characteristic combined matching, and the method has the advantages of high identification efficiency and small error in the application process.
Drawings
FIG. 1 is a flow chart of a multi-class exemplary load feature analysis extraction method of the present invention;
FIG. 2 is a typical harmonic spectrum diagram of a wall-mounted fixed-frequency air conditioner according to an embodiment of the present invention;
FIG. 3 is a transient waveform diagram of current at the moment of loading a certain electric appliance according to an embodiment of the present invention;
Fig. 4 is an intermittent operation power diagram of the electric cooker according to the embodiment of the invention.
Detailed Description
The application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
Referring to fig. 1, a multi-class typical load feature analysis and extraction method includes:
and step 1, collecting multidimensional electricity utilization data of the load side of the electric energy meter.
Specifically, in step 1, the multidimensional electricity data includes: steady state active power, steady state reactive power, steady state harmonic current, and transient current.
In the preferred embodiment, the multidimensional electricity data is obtained through high-frequency sampling, and the active power and the reactive power of the load of the residential electric appliance directly reflect the real-time working state, the electric energy consumption level and the load characteristic of the household electric appliance, so that the real-time working state, the electric energy consumption level and the load characteristic of the household electric appliance are basic characteristic parameters of the electric appliance working in a steady state stage. The active power and the reactive power can be calculated through the high-frequency sampling current and the high-frequency sampling voltage.
Step 2, calculating the active power of the load by utilizing the multidimensional electricity consumption data; the occurrence time of the load action event is monitored based on the change condition of the active power.
Specifically, step 2 includes:
In step 2.1, since the voltage and the current in the high-frequency sampling data are discrete values, the active power P is calculated by adopting fourier decomposition, and the following relation is satisfied:
Wherein U k is a voltage effective value, I k is a current effective value, k is a harmonic order, and k=0, 1, 2; phi k is the power factor angle corresponding to each harmonic frequency;
there are obvious differences in current waveforms of different home appliances. Specifically, the resistive appliance current waveform exhibits a standard sinusoidal curve; the inductive and capacitive appliance current waveforms are also substantially sinusoidal, but the phases will exhibit varying degrees of hysteresis and lead; the electrical appliance with the power electronic equipment is nonlinear equipment, and a current curve has a large number of sharp corners and flat tops. To reflect the difference of the current waveforms of all the electric equipment, the root mean square, the amplitude, the crest factor and the total harmonic distortion of the current waveforms of all the electric equipment are calculated and extracted.
And 2.2, monitoring a change value delta P of the active power in the current period relative to the previous period, and when the absolute value of the change value delta P is larger than a preset threshold value, determining that a load action event occurs in the current period, and recording the occurrence time of the load action event.
And 3, respectively extracting multidimensional electricity utilization data in t periods forwards and backwards by taking the occurrence time of the load action event as a reference.
Specifically, in step 3, the number t of periods is not less than 6.
And 4, analyzing and calculating to obtain steady-state characteristic data and transient-state characteristic data of the load according to the multidimensional electricity consumption data.
Specifically, in step4, the steady-state feature data includes: active power, reactive power, current waveform characteristics, V-I track characteristics, harmonic content and harmonic combination sequencing.
Specifically, the reactive power Q is calculated by fourier decomposition, satisfying the following relation:
Wherein U k is a voltage effective value, I k is a current effective value, k is a harmonic order, and k=0, 1, 2; phi k is the power factor angle corresponding to each harmonic order.
Specifically, the current waveform characteristics include: root mean square I rms, amplitude I P, crest factor I cf and total harmonic distortion THD;
Wherein, root mean square I rms satisfies the following relation:
Wherein m is a counting index, N is the number of sampling points, and i m is the instantaneous current of the sampling points;
Wherein, the amplitude I P satisfies the following relation:
IP＝max(im)0≤m≤N
Wherein, the crest factor I cf satisfies the following relation:
Wherein, the total harmonic distortion THD satisfies the following relation:
Where k is the harmonic order, I k is the kth harmonic current, and I 1 is the fundamental current.
The definition of the V-I track is that the instantaneous values of the voltage and the current when the electric appliance works are drawn on the same coordinate axis, and a special image, namely a V-I track graph, can be obtained. The V-I track graph of the resistor load is a straight line; the V-I track pattern of the inductive capacitive load is an ellipse; if the V-I trace is plotted for only 3 harmonic currents, it must have 2 intersections. Therefore, the V-I track graph curve can reflect the height of harmonic components contained in the current from the graph angle, and if the drawn V-I track graph has intersection points, the harmonic content is necessarily very high; if the pattern is very different from the elliptical shape, the harmonic content must also be very high.
The V-I track patterns of different loads have obvious differences, and characteristic description parameters such as the number of cross points, the slope of a central line, the closing area and the like can be selected as the characteristics of the V-I track patterns. For example, the V-I track patterns of appliances such as air conditioners, microwave ovens and the like have intersection points, and appliances such as hot water pots and the like do not have intersection points, so the number of the intersection points can be taken as the description parameters of the characteristics of the V-I track patterns; the central line inclination angles of the curves are also different, and the central line corresponds to the connecting line of the highest point and the lowest point in the V-I track graph, so that the slope of the central line can also be used as the characteristic description parameter of the V-I track graph characteristic; the V-I track graph is a closed curve, and the areas surrounded by the V-I track graph are different from each other in different loads, so that the area of the V-I track graph can be taken as a measurement parameter.
Thus, the V-I trajectory features include: the number of curve crossing points, the slope of the curve center line and the curve closure area.
Specifically, a harmonic analysis algorithm based on fast Fourier transform redesigns harmonic component data into multiple groups to be combined and ordered, so that harmonic combination ordering is obtained; the harmonic combination ordering includes direct current components, low odd harmonics, medium odd harmonics, high odd harmonics, low even harmonics, medium even harmonics and high even harmonics.
The harmonic content analysis can quantitatively decompose the proportion of each subharmonic, thereby describing the characteristics among different electric appliance loads in detail. Considering that the high-frequency sampling data are discrete data, the discrete fast Fourier transform is adopted, and the harmonic content up to 15 times can be calculated from the original load data, so that the proportion of each harmonic in different electric appliances and different working modes is obtained.
As can be seen from fig. 2, the fixed-frequency air conditioning equipment has various component harmonics during operation, and the harmonic characteristics of the fixed-frequency air conditioning equipment mainly have two aspects: firstly, odd harmonics are the main ones and the amplitude of the harmonics is gradually reduced along with the harmonic frequency; and secondly, the ratio of the direct current component to the even harmonic component is small and can be ignored.
For better exploitation of the rules in the harmonic characteristics, stable features are formed. The harmonic component data is redesigned into multiple groups based on a harmonic analysis algorithm of fast Fourier transform, and the groups specifically comprise direct current components (0 th order), low odd harmonics (3 rd order and 5 th order), medium odd harmonics (7 th order and 9 th order), high odd harmonics (11 th order and 13 th order), low even harmonics (2 th order and 4 th order), medium even harmonics (6 th order and 8 th order) and high even harmonics (10 th order and 12 th order). The harmonic combination ranking characteristics are studied below using an induction cooker or a microwave oven as an example, and the analysis results are shown in tables 1 to 4.
Table 1 table of harmonic data for steady state stage after induction cooker input event
In table 1, the harmonic order 1 represents a low even harmonic, 2 represents a medium even harmonic, 3 represents a high even harmonic, 4 represents a low odd harmonic, 5 represents a medium odd harmonic, and 6 represents a high odd harmonic.
Table 2 table of harmonic data for steady state stage prior to an induction cooker ablation event
In table 2, the harmonic order 1 represents a low even harmonic, 2 represents a medium even harmonic, 3 represents a high even harmonic, 4 represents a low odd harmonic, 5 represents a medium odd harmonic, and 6 represents a high odd harmonic.
Table 3 table of harmonic data for steady state stage after microwave oven input event
In table 3, the harmonic order 1 represents a low even harmonic, 2 represents a medium even harmonic, 3 represents a high even harmonic, 4 represents a low odd harmonic, 5 represents a medium odd harmonic, and 6 represents a high odd harmonic.
Table 4 table of harmonic data for steady state stage prior to microwave ablation event
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In table 4, the harmonic order 1 represents a low even harmonic, 2 represents a medium even harmonic, 3 represents a high even harmonic, 4 represents a low odd harmonic, 5 represents a medium odd harmonic, and 6 represents a high odd harmonic.
Analysis of the above data may reveal that: the harmonic component of the microwave oven electric appliance is particularly large, the low frequency is more than 1.5 times of the fundamental wave, the direct current component of the induction cooker with obvious characteristic abnormality is less than 0.01, and the order of odd frequency (456) and even frequency (123) can be approximately generated in the harmonic order. The odd and even harmonic content appears to fluctuate somewhat inside the packet, but is generally stable. Thus, analysis shows that the harmonic content and the combined ordering of the harmonics can be used as a better steady state feature.
In this embodiment, the transient characteristics include transient duration, transient current step height and surge height, and surge coefficient. The transient characteristics of the electrical load have a stronger adaptability in distinguishing different types of electrical appliances, as distinguished from the steady-state characteristics. Because the superposition condition of multiple electric appliances can occur in the steady-state process, the superposition of steady-state characteristics of the multiple electric appliances is directly caused, corresponding electric appliances cannot be directly identified according to the steady-state characteristics, and characteristic parameter separation is needed. However, the transient duration of the load is much shorter than the steady state duration, and the probability of feature overlap during the transient phase is much smaller than during the steady state phase, so that different appliances can be distinguished according to the transient features.
Of all the transient characteristics, the most typical transient characteristic is the switching transient characteristic of the electrical load generated at the time of switching in and switching out.
As can be seen from FIG. 3, the transient event of the electrical appliance is very short, and the time from the tiny current to the steady current state is 6 power frequency cycles (0.12 s). Meanwhile, the load input transient state presents typical spike pulse, and the peak value, the step height and the fall-back degree of the spike pulse are typical characteristics of the transient state stage, so that different electric appliances can be directly distinguished according to the characteristics.
Specifically, in step 4, the transient characteristic data includes: transient duration, transient current step height and strike height, and strike coefficient.
Wherein the transient duration Δt satisfies the following relationship:
ΔT＝Tet-Tst
wherein, transient current step height Δi steady satisfies the following relation:
ΔIsteady＝Iet-Ist
wherein the impact height Δi impulse satisfies the following relation:
ΔIimpulse＝Imax-Ist
wherein the impact coefficient V satisfies the following relation:
Wherein, T et is the transient start time, T st is the transient end time, I et is the current value at the transient start time, I st is the current value at the transient end time, and I max is the maximum current value in the transient phase. The impact coefficient V is the ratio of the transient current impact height to the step height and reflects the characteristics of the spike pulse
And 5, constructing a time sequence sliding window, and extracting non-electrical characteristic data of the load which is matched with the steady-state characteristic data and the transient characteristic data in a joint way.
Specifically, the non-electrical characteristic data includes: time characteristics of intermittent operation of load, seasonal characteristics of intermittent operation of load, and related characteristics of intermittent operation of load.
In this embodiment, the non-electrical characteristics include load intermittent run time characteristics, seasonal characteristics, and association characteristics. Here, mainly attention is paid to the intermittent characteristic of the operation of the electric appliance, and the electric appliance with the intermittent operation time characteristic can adjust the operation time of the electric appliance according to the control strategy of the internal controller, so that the intermittent operation is realized. For example, the water heater may be automatically turned on and off according to the temperature controller. When the heated water temperature reaches a set temperature threshold value, the water heater stops running; when the water temperature in the water tank reaches the lower limit threshold value of the controller, the water heater can be started to run again, and the operation is repeated. At this time, the active power of the electrical load exhibits a time characteristic of intermittent operation.
Different electric appliances have different regulation and control strategies, the electric water heater designs the regulation and control strategies according to the relation between the water temperature and the set threshold, the electric rice cooker designs the regulation and control strategies according to different cooking conditions, and fig. 4 is an intermittent operation power diagram of the electric rice cooker.
As can be seen from fig. 4, the standard boiling mode of the electric cooker shows a plurality of intermittent operation, and the intermittent operation time and frequency are not consistent, which is related to the working operation strategy designed by the manufacturer of the electric cooker. The standard boiling mode of the electric cooker has 5 stages, namely preheating, water absorption, heating, boiling and stewing. The electric cooker only presents a continuous long-time working state in a heating stage, the rest 4 stages are all operated intermittently, and the time interval and the times of intermittent operation are different.
Further, in order to unify and standardize the characteristic parameters of intermittent operation of the resident load, the intermittent operation times and the intermittent operation duration parameters in unit time are designed, and in step 5, the time characteristics of intermittent operation of the load are extracted, including:
Step 5.1, constructing sliding windows in unit time, wherein each small window comprises n discrete power points;
Step 5.2, monitoring all load action events in the sliding window; wherein the load action event comprises: a load throw-in event and a load cut-out event;
Step 5.3, calculating the number of intermittent load operation times in unit time;
step 5.4, calculating the duration of intermittent load operation in unit time;
and 5.5, sliding n discrete power points to form a new sliding window, and repeating the step 5.2.
Further, in step 5.3, the number of intermittent load operations per unit time is the sum of the number of load input events and load removal events in the sliding window divided by 2.
Further, in step 5.4, the duration of the intermittent load operation in unit time is the sum of the time intervals of all load shedding events and the next load throwing event.
Compared with the prior art, the method has the advantages that the defects of load detection and identification by single characteristics are overcome through extracting the steady-state characteristics, the transient characteristics and the non-electrical characteristics of the household load and carrying out multi-characteristic combined matching, and the method has the advantages of high identification efficiency and small error in the application process.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (15)
1. A multi-category typical load characteristic analysis and extraction method is characterized in that,
The method comprises the following steps:
Step 1, collecting multidimensional electricity utilization data of a load side of an electric energy meter;
Step 2, calculating the active power of the load by utilizing the multidimensional electricity utilization data; monitoring the occurrence time of a load action event based on the change condition of the active power;
Step 3, taking the occurrence time of the load action event as a reference, respectively extracting multidimensional electricity data in t periods forwards and backwards;
step 4, analyzing and calculating to obtain steady-state characteristic data and transient-state characteristic data of the load according to the multidimensional electricity consumption data; wherein the steady state characteristic data comprises: active power, reactive power, current waveform characteristics, V-I track characteristics, harmonic content and harmonic combination sequencing; the transient characteristic data includes: transient duration, transient current step height and impact height, impact coefficient;
Step 5, constructing a time sequence sliding window, and extracting non-electrical characteristic data of the load which is matched with the steady-state characteristic data and the transient characteristic data in a joint way; wherein the non-electrical characteristics include load intermittent run time characteristics, seasonal characteristics, and associative characteristics.
2. A multi-class representative load feature analysis and extraction method according to claim 1, wherein,
In step 1, the multi-dimensional electricity consumption data includes: high frequency sampled current and voltage, steady state harmonic current and transient current.
3. A multi-class representative load feature analysis and extraction method according to claim 2, wherein,
The step 2 comprises the following steps:
Step 2.1, calculating active power P by adopting Fourier decomposition, wherein the following relation is satisfied:
Wherein U k is a voltage effective value, I k is a current effective value, k is a harmonic order, and k=0, 1, 2; the power factor angle corresponding to each harmonic frequency is set;
And 2.2, monitoring a change value delta P of the active power in the current period relative to the previous period, and when the absolute value of the change value delta P is larger than a preset threshold value, determining that a load action event occurs in the current period and recording the occurrence period of the load action event.
4. A multi-class representative load feature analysis and extraction method according to claim 1, wherein,
In step 3, the number t of the periods is not less than 6.
5. A multi-class representative load feature analysis and extraction method according to claim 1, wherein,
In step 4, the steady-state feature data includes: active power, reactive power, current waveform characteristics, V-I track characteristics, harmonic content and harmonic combination sequencing.
6. A multi-category representative load signature analysis and extraction method as defined in claim 5, wherein,
And calculating reactive power Q by adopting Fourier decomposition, and satisfying the following relation:
Wherein U k is a voltage effective value, I k is a current effective value, k is a harmonic order, and k=0, 1, 2; And the power factor angle is corresponding to each harmonic frequency.
7. A multi-category representative load signature analysis and extraction method as defined in claim 5, wherein,
The current waveform characteristics include: root mean square I rms, amplitude I P, crest factor I cf and total harmonic distortion THD;
Wherein, root mean square I rms satisfies the following relation:
Wherein m is a counting index, N is the number of sampling points, and i m is the instantaneous current of the sampling points;
Wherein, the amplitude I P satisfies the following relation:
IP＝max(im) 0≤m≤N
Wherein, the crest factor I cf satisfies the following relation:
Wherein, the total harmonic distortion THD satisfies the following relation:
Where k is the harmonic order, I k is the kth harmonic current, and I 1 is the fundamental current.
8. A multi-category representative load signature analysis and extraction method as defined in claim 5, wherein,
The V-I trajectory feature includes: the number of curve crossing points, the slope of the curve center line and the curve closure area.
9. A multi-category representative load signature analysis and extraction method as defined in claim 5, wherein,
Redesign the harmonic component data into multiple groups based on a harmonic analysis algorithm of the fast Fourier transform, and sequencing to obtain harmonic combination sequencing;
the harmonic combination ordering includes direct current components, low odd harmonics, medium odd harmonics, high odd harmonics, low even harmonics, medium even harmonics and high even harmonics.
10. A multi-class representative load feature analysis and extraction method according to claim 1, wherein,
In step 4, the transient characteristic data includes: transient duration, transient current step height and strike height, and strike coefficient.
11. The method for extracting multi-category representative load characteristics analysis according to claim 10, wherein,
The transient duration Δt satisfies the following relationship:
ΔT＝Tet-Tst
The transient current step height Δi steady satisfies the following relationship:
ΔIsteady＝Iet-Ist
The impact height Δi impulse satisfies the following relation:
ΔIimpulse＝Imax-Ist
the impact coefficient V satisfies the following relation:
wherein, T et is the transient start time, T st is the transient end time, I et is the current value at the transient start time, I st is the current value at the transient end time, and I max is the maximum current value in the transient phase.
12. A multi-class representative load feature analysis and extraction method according to claim 1, wherein,
The non-electrical characteristic data includes: time characteristics of intermittent operation of load, seasonal characteristics of intermittent operation of load, and related characteristics of intermittent operation of load.
13. The method for extracting multi-class typical load characteristics analysis according to claim 12, wherein,
In step 5, extracting the time characteristic of the intermittent operation of the load active power, including:
step 5.1, constructing sliding windows in unit time, wherein each small window comprises n discrete active power points;
Step 5.2, monitoring all load action events in the sliding window; wherein the load action event comprises: a load throw-in event and a load cut-out event; the method comprises the steps of monitoring a change value delta P of active power in a current period relative to a previous period, determining that a load action event occurs in the current period when the absolute value of the change value delta P is larger than a preset threshold value, and recording the occurrence period of the load action event;
Step 5.3, calculating the number of intermittent load operation times in unit time;
step 5.4, calculating the duration of intermittent load operation in unit time;
and 5.5, sliding n discrete power points to form a new sliding window, and repeating the step 5.2.
14. The method for extracting multi-class typical load characteristics analysis according to claim 13, wherein,
In step 5.3, the number of intermittent load running times in unit time is the sum of the number of load input events and load removal events in the sliding window divided by 2.
15. The method for extracting multi-class typical load characteristics analysis according to claim 13, wherein,
In step 5.4, the duration of the intermittent load operation in unit time is the sum of the time intervals of all load shedding events and the next load input event.
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