CN113408481A - Multi-class typical load characteristic analysis and extraction method - Google Patents
Multi-class typical load characteristic analysis and extraction method Download PDFInfo
- Publication number
- CN113408481A CN113408481A CN202110791277.0A CN202110791277A CN113408481A CN 113408481 A CN113408481 A CN 113408481A CN 202110791277 A CN202110791277 A CN 202110791277A CN 113408481 A CN113408481 A CN 113408481A
- Authority
- CN
- China
- Prior art keywords
- load
- harmonic
- transient
- current
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 31
- 238000000605 extraction Methods 0.000 title claims abstract description 23
- 230000001052 transient effect Effects 0.000 claims abstract description 56
- 238000000034 method Methods 0.000 claims abstract description 28
- 230000009471 action Effects 0.000 claims abstract description 20
- 230000005611 electricity Effects 0.000 claims abstract description 11
- 230000008859 change Effects 0.000 claims abstract description 10
- 238000012544 monitoring process Methods 0.000 claims abstract description 10
- 238000005070 sampling Methods 0.000 claims description 9
- 238000012163 sequencing technique Methods 0.000 claims description 7
- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 230000001932 seasonal effect Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 abstract description 8
- 230000007547 defect Effects 0.000 abstract description 5
- 230000008569 process Effects 0.000 abstract description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 10
- 238000010586 diagram Methods 0.000 description 4
- 230000033228 biological regulation Effects 0.000 description 3
- 238000010411 cooking Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 241000209094 Oryza Species 0.000 description 2
- 235000007164 Oryza sativa Nutrition 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 230000001939 inductive effect Effects 0.000 description 2
- 235000009566 rice Nutrition 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 238000009835 boiling Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Images
Classifications
-
- 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
- G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
- G01R21/001—Measuring real or reactive component; Measuring apparent energy
- G01R21/002—Measuring real component
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
- G01R21/001—Measuring real or reactive component; Measuring apparent energy
- G01R21/003—Measuring reactive component
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
- G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
-
- 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
Abstract
A multi-class typical load feature analysis and extraction method comprises the following steps: acquiring multi-dimensional electricity consumption data of the load side of the electric energy meter; calculating the active power of the load by using the multi-dimensional power consumption data; monitoring the occurrence time of a load action event based on the change condition of active power; respectively extracting multi-dimensional power utilization data in t periods forwards and backwards by taking the occurrence time of a load action event as a reference; analyzing and calculating to obtain steady-state characteristic data and transient-state characteristic data of the load according to the multi-dimensional power utilization data; and constructing a time series sliding window, and extracting the non-electrical characteristic data of the load jointly matched with the steady-state characteristic data and the transient-state characteristic data. The method overcomes the defect of load detection and identification by single characteristic by extracting the steady-state characteristic, the transient characteristic and the non-electric characteristic of the household load and combining and 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 intelligent power grids, in particular to a multi-class typical load characteristic analysis and extraction method.
Background
With the maturity of the next generation electric energy meter technology, the non-intrusive power load identification becomes one of the essential functions of the electricity meter of the internet of things, domestic mainstream electric meter manufacturers and new electricity technology research enterprises begin to invest a large amount of manpower and material resources to pre-research the non-intrusive load identification technology, and the researches rely on an electric appliance load data sample library, so that an objective external market for demands of a resident load data acquisition device is formed.
In the intrusive home load identification technique, the characteristics of the load are the basis for the analysis of the technique. Generally, the household load is characterized by two categories, steady state and transient state. The former is characteristic quantity which can be extracted when the equipment is in a stable operation state, the latter is characteristic information which is shown when the equipment is switched, extraction aiming at non-electrical characteristics is lacked, and the existing steady-state and transient-state characteristics show the problems of low identification efficiency, large error, poor expansibility and the like in the 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 load detection accuracy and the feasibility are low, and the load detection cannot be used as data support of a smart grid.
In summary, the load features need to be extracted in an all-around manner, and the load features of multiple types are effectively matched, so as to realize accurate detection and identification of the load.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a multi-class typical load feature analysis and extraction method, which realizes the joint matching of multi-class load features by extracting the steady-state features, the transient features and the non-electrical features of the load and overcomes the defect that the load detection and identification are carried out only by using a single load feature.
The invention adopts the following technical scheme.
A multi-class typical load feature analysis and extraction method comprises the following steps:
step 1, collecting multi-dimensional electricity utilization data of a load side of an electric energy meter;
step 3, taking the occurrence time of the load action event as a reference, and respectively extracting multi-dimensional power utilization data in t periods forwards and backwards;
and 5, constructing a time sequence sliding window, and extracting the non-electrical characteristic data of the load jointly matched with the steady-state characteristic data and the transient characteristic data.
Preferably, in step 1, the multidimensional 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 the active power P by adopting Fourier decomposition, and satisfying the following relational expression:
in the formula of UkIs an effective value of voltage, IkIs the effective value of the current, k is the harmonic frequency, and k is 0,1, 2; phi is akA 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, determining that a load action event occurs in the current period when the absolute value of the change value delta P is greater than a preset threshold value, and recording the occurrence time of the load action event.
Preferably, in step 3, the number t of cycles is not less than 6.
Preferably, 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.
Preferably, the reactive power Q is calculated by fourier decomposition, satisfying the following relation:
in the formula of UkIs an effective value of voltage, IkIs the effective value of the current, k is the harmonic frequency, and k is 0,1, 2; phi is akThe power factor angle corresponding to each harmonic frequency.
Preferably, the current waveform characteristics include: root mean square IrmsAmplitude IPCrest factor IcfAnd a total harmonic distortion rate THD;
wherein mean squareRoot IrmsSatisfies the following relation:
wherein m is a counting subscript, N is the number of sampling points, imIs the instantaneous current at the sampling point;
wherein the amplitude value IPSatisfies the following relation:
IP＝max(im)0≤m≤N
wherein, the crest factor IcfSatisfies the following relation:
wherein, the total harmonic distortion rate THD satisfies the following relation:
wherein k is the harmonic order, IkIs the kth harmonic current, I1Is the fundamental current.
Preferably, the V-I trajectory features include: the number of curve intersections, the slope of the curve centerline, and the curve closure area.
Preferably, the harmonic component data is redesigned into multiple combinations and sequenced based on a harmonic analysis algorithm of fast Fourier transform, so as to obtain harmonic combination sequencing; the harmonic combination order includes a direct current component, a low odd harmonic, a medium odd harmonic, a high odd harmonic, a low even harmonic, a medium even harmonic, and a high even harmonic.
Preferably, in step 4, the transient characteristic data includes: transient duration, transient current step height and impact height, impact coefficient.
Wherein the transient duration Δ T satisfies the following relation:
ΔT＝Tet-Tst
wherein the transient current step height Δ IsteadySatisfies the following relation:
ΔIsteady＝Iet-Ist
wherein the impact height Δ IimpulseSatisfies the following relation:
ΔIimpulse＝Imax-Ist
wherein the impact coefficient V satisfies the following relation:
in the formula, TetIs the transient start time, TstAs the end of the transient, IetIs the current value at the transient start time, IstCurrent value at the end of transient, ImaxIs the maximum current value in the transient phase.
Preferably, the non-electrical characteristic data comprises: the time characteristic of the intermittent operation of the load, the seasonal characteristic of the intermittent operation of the load and the associated characteristic of the intermittent operation of the load.
Preferably, in step 5, the extracting of the time characteristic of the intermittent operation of the load includes:
step 5.1, constructing a unit time sliding window, 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-on event and a load-off event;
step 5.3, calculating the number of times of intermittent operation of the load in unit time;
step 5.4, calculating the time length of intermittent operation of the load in unit time;
and 5.5, sliding the n discrete power points to form a new sliding window, and repeating the step 5.2.
Further, in step 5.3, the number of times of the intermittent operation of the load in the unit time is the sum of the number of times of the load input event and the load removal event in the sliding window divided by 2.
Further, in step 5.4, the time duration of the intermittent operation of the load in unit time is the sum of the time intervals of all load shedding events and the next load input event.
Compared with the prior art, the method has the advantages that the defects of single characteristic load detection and identification are overcome by extracting the steady-state characteristic, the transient characteristic and the non-electrical characteristic of the household load and combining and matching multiple characteristics, and the method has the advantages of high identification efficiency and small error in the application process.
Drawings
FIG. 1 is a schematic flow chart of a multi-class typical load characteristic analysis and extraction method according to 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 waveform diagram of a transient state of current at a moment when a load of an electrical appliance is put into operation according to an embodiment of the present invention;
FIG. 4 is a power diagram of the intermittent operation of the electric rice cooker according to the embodiment of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Referring to fig. 1, a method for analyzing and extracting multi-class typical load features includes:
step 1, collecting multi-dimensional electricity consumption data of a load side of an electric energy meter.
Specifically, in step 1, the multidimensional electricity consumption data includes: steady state active power, steady state reactive power, steady state harmonic current, and transient current.
In the preferred embodiment, the multidimensional electricity utilization data is obtained through high-frequency sampling, and the active power and the reactive power of the load of the residential electrical appliance directly reflect the real-time working state, the electric energy consumption level and the load characteristic of the household electrical appliance and are basic characteristic parameters of the electrical appliance in a steady-state working stage. And calculating active power and reactive power through the high-frequency sampled current and voltage.
Specifically, step 2 comprises:
step 2.1, because 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 relational expression is satisfied:
in the formula of UkIs an effective value of voltage, IkIs the effective value of the current, k is the harmonic frequency, and k is 0,1, 2; phi is akA power factor angle corresponding to each harmonic frequency;
the current waveforms of different household appliances are obviously different. Specifically, the current waveform of the resistive electrical appliance presents a standard sine curve; the current waveforms of the inductive and capacitive appliances are basically sinusoidal, but the phases can show different degrees of lag and lead; the electric appliance with the power electronic equipment is a nonlinear equipment, and a current curve has a large number of sharp corners and flat tops. In order 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, determining that a load action event occurs in the current period when the absolute value of the change value delta P is greater than a preset threshold value, and recording the occurrence time of the load action event.
And 3, respectively extracting the 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 cycles is not less than 6.
And 4, analyzing and calculating to obtain the steady-state characteristic data and the transient-state characteristic data of the load according to the multi-dimensional power utilization data.
Specifically, 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.
Specifically, the reactive power Q is calculated by fourier decomposition, and the following relation is satisfied:
in the formula of UkIs an effective value of voltage, IkIs the effective value of the current, k is the harmonic frequency, and k is 0,1, 2; phi is akThe power factor angle corresponding to each harmonic frequency.
Specifically, the current waveform characteristics include: root mean square IrmsAmplitude IPCrest factor IcfAnd a total harmonic distortion rate THD;
wherein, root mean square IrmsSatisfies the following relation:
wherein m is a counting subscript, N is the number of sampling points, imIs the instantaneous current at the sampling point;
wherein the amplitude value IPSatisfies the following relation:
IP＝max(im)0≤m≤N
wherein, the crest factor IcfSatisfies the following relation:
wherein, the total harmonic distortion rate THD satisfies the following relation:
wherein k is the harmonic order, IkIs the kth harmonic current, I1Is the fundamental current.
The definition of the V-I track is that a special image, namely a V-I track graph, can be obtained by drawing the voltage and current instantaneous values of the electric appliance during working on the same coordinate axis. The V-I track graph of the resistance load is a straight line; the V-I track graph of the inductive and 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 can reflect the harmonic content in the current from the graph angle, and if the drawn V-I track graph has an intersection point, the harmonic content is necessarily high; if the pattern is very different from the elliptical shape, the harmonic content is inevitably high.
The V-I track graphs of different loads have obvious difference, and the characteristic description parameters such as the number of cross points, the slope of a central line, the closed area and the like can be selected as the characteristics of the V-I track graphs. For example, V-I track graphs of electric appliances such as air conditioners, microwave ovens and the like have intersection points, and electric appliances such as hot water kettles and the like do not have intersection points, so that the number of the intersection points can be taken as description parameters of the characteristics of the V-I track graphs; the inclination angles of the center lines of all the curves are different, and the center lines correspond to the connecting lines of the highest points and the lowest points in the V-I track graph, so that the inclination of the center lines can also be used as characteristic description parameters of the characteristics of the V-I track graph; the V-I track graph is a closed curve, and the areas enclosed by the V-I track graphs under different loads are different, 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 intersections, the slope of the curve centerline, and the curve closure area.
Specifically, harmonic component data is redesigned into multiple combinations and sequenced based on a harmonic analysis algorithm of fast Fourier transform, so that harmonic combination sequencing is obtained; the harmonic combination order includes a direct current component, a low odd harmonic, a medium odd harmonic, a high odd harmonic, a low even harmonic, a medium even harmonic, and a high even harmonic.
The harmonic content analysis can quantitatively decompose the proportion of each harmonic, thereby describing the characteristics among different electrical appliance loads in detail. Considering that the high-frequency sampling data is discrete data, the content of the harmonic wave up to 15 times can be calculated from the original load data by adopting discrete fast Fourier transform, and further the proportion of each harmonic wave under different electrical appliances and different working modes can be obtained.
As can be seen from fig. 2, the fixed-frequency air conditioning equipment has a plurality of component harmonics during operation, and the harmonic characteristics thereof have two main aspects: firstly, odd harmonic is dominant, and the amplitude of the harmonic is gradually reduced along with the number of the harmonic; and secondly, the ratio of the direct current component to the even harmonic component is very small and can be ignored.
In order to better mine the regularity in the harmonic characteristics, a stable feature is formed. The harmonic analysis algorithm based on fast Fourier transform redesigns harmonic component data into multiple combinations and orders the multiple combinations, wherein the combinations specifically comprise direct current components (0), low odd harmonics (3, 5), medium odd harmonics (7, 9), high odd harmonics (11, 13), low even harmonics (2, 4), medium even harmonics (6, 8) and high even harmonics (10, 12). The following examples of electromagnetic oven and microwave oven are provided to study the harmonic combination ranking characteristics, and the analysis results are shown in tables 1 to 4.
TABLE 1 Steady State harmonic data sheet 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 harmonic data sheet for steady state phase before electromagnetic oven removal 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 harmonic data sheet for steady state phase after microwave oven commissioning event
In table 3, the harmonic number 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 harmonic datasheet for steady state phase before microwave oven removal event
In table 4, 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.
The analysis of the data can find that: the harmonic component of microwave oven electric appliance is very large, its low level is 1.5 times of fundamental wave, its characteristic abnormal obvious electromagnetic oven direct current components are less than 0.01, and the harmonic ordering can approximately produce odd order (456) -even order (123) ordering. The odd and even harmonic content appears to fluctuate somewhat within the packet, but the overall stability is stable. Therefore, the harmonic content and the combined order of the harmonics can be used as a better steady-state feature through analysis.
In this embodiment, the transient characteristics include a transient duration, a transient current step height, a transient current bump height, and a bump coefficient. Different from the steady-state characteristic, the transient characteristic of the electric appliance load has stronger adaptability in the aspect of distinguishing different kinds of electric appliances. Because the situation of superposition of multiple electric appliances may occur in the steady-state process, the superposition of steady-state characteristics of multiple electric appliances is directly caused, so that the corresponding electric appliances cannot be directly identified according to the steady-state characteristics, and characteristic parameter separation is also needed. However, the transient duration of the load is much shorter than the steady-state duration, and the probability of the overlapping of the characteristics in the transient phase is much smaller than in the steady-state phase, so that different appliances can be distinguished according to the transient characteristics.
Among all transient characteristics, the most typical one is the switching transient characteristic generated by the appliance load at the moment of switching-in and switching-off.
As can be seen from fig. 3, the transient event of the appliance is very short, from a small current to a steady current state, for 6 power frequency cycles (0.12 s). Meanwhile, the load input transient state presents typical spike pulse, the peak value, the step height and the falling degree of the spike pulse are typical characteristics of the transient state stage, and 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 impact height, impact coefficient.
Wherein the transient duration Δ T satisfies the following relation:
ΔT＝Tet-Tst
wherein the transient current step height Δ IsteadySatisfies the following relation:
ΔIsteady＝Iet-Ist
wherein the impact height Δ IimpulseSatisfies the following relation:
ΔIimpulse＝Imax-Ist
wherein the impact coefficient V satisfies the following relation:
in the formula, TetIs the transient start time, TstAs the end of the transient, IetIs the current value at the transient start time, IstCurrent value at the end of transient, ImaxIs 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 the non-electrical characteristic data of the load jointly matched with the steady-state characteristic data and the transient characteristic data.
Specifically, the non-electrical characteristic data includes: the time characteristic of the intermittent operation of the load, the seasonal characteristic of the intermittent operation of the load and the associated characteristic of the intermittent operation of the load.
In this embodiment, the non-electrical characteristics include load intermittent operation time characteristics, seasonal characteristics, and correlation characteristics. The intermittent operation characteristic of the electric appliance is mainly concerned, and the electric appliance with the intermittent operation characteristic can adjust the operation time of the electric appliance according to the control strategy of the internal controller of the electric appliance, so that the intermittent operation is realized. For example, a water heater may automatically start and stop according to a 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 again to operate, and the operation is repeated. At this time, the active power of the electric load exhibits a time characteristic of intermittent operation.
Different electric appliances have different regulation strategies, the electric water heater designs the regulation strategy according to the relation between the water temperature and the set threshold value, the electric cooker designs the regulation strategy according to different working conditions of cooking, and fig. 4 is an intermittent operation power diagram of the electric cooker.
As can be seen from FIG. 4, the electric cooker exhibits a plurality of intermittent operations in the standard cooking mode, and the intermittent operation time is not consistent with the frequency and is related to the work operation strategy designed by the electric appliance manufacturer. The standard cooking mode of the electric cooker has 5 stages, namely preheating, water absorption, heating, boiling and rice stewing. The electric cooker only presents a continuous long-time working state in the heating stage, the rest 4 stages are all operated intermittently, and the time interval and the times of the intermittent operation are different.
Further, in order to unify and standardize the characteristic parameters of the load intermittent operation of the residents, the parameters of the number of intermittent operation times and the length of the intermittent operation time in unit time are designed, and in the step 5, the time characteristic of the load intermittent operation is extracted, which comprises the following steps:
step 5.1, constructing a unit time sliding window, 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-on event and a load-off event;
step 5.3, calculating the number of times of intermittent operation of the load in unit time;
step 5.4, calculating the time length of intermittent operation of the load in unit time;
and 5.5, sliding the n discrete power points to form a new sliding window, and repeating the step 5.2.
Further, in step 5.3, the number of times of the intermittent operation of the load in the unit time is the sum of the number of times of the load input event and the load removal event in the sliding window divided by 2.
Further, in step 5.4, the time duration of the intermittent operation of the load in unit time is the sum of the time intervals of all load shedding events and the next load input event.
Compared with the prior art, the method has the advantages that the defects of single characteristic load detection and identification are overcome by extracting the steady-state characteristic, the transient characteristic and the non-electrical characteristic of the household load and combining and matching multiple characteristics, and the method has the advantages of high identification efficiency and small error in the application process.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely 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 for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (15)
1. A multi-class typical load feature analysis and extraction method is characterized in that,
the method comprises the following steps:
step 1, collecting multi-dimensional electricity utilization data of a load side of an electric energy meter;
step 2, calculating the active power of the load by using the multi-dimensional power 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, and respectively extracting multi-dimensional power utilization data in t periods forwards and backwards;
step 4, analyzing and calculating to obtain the steady-state characteristic data and the transient-state characteristic data of the load according to the multi-dimensional power utilization data;
and 5, constructing a time sequence sliding window, and extracting the non-electrical characteristic data of the load jointly matched with the steady-state characteristic data and the transient characteristic data.
2. The method according to claim 1, wherein the multi-class typical load characteristic analysis and extraction method,
in step 1, the multidimensional electricity consumption data includes: steady state active power, steady state reactive power, steady state harmonic current, and transient current.
3. The method according to claim 2, wherein the multi-class typical load characteristic analysis and extraction method,
the step 2 comprises the following steps:
step 2.1, calculating the active power P by adopting Fourier decomposition, and satisfying the following relational expression:
in the formula of UkIs an effective value of voltage, IkIs the effective value of the current, k is the harmonic frequency, and k is 0,1, 2; phi is akA 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, determining that a load action event occurs in the current period when the absolute value of the change value delta P is greater than a preset threshold value, and recording the occurrence time of the load action event.
4. The method according to claim 1, wherein the multi-class typical load characteristic analysis and extraction method,
in step 3, the number t of the periods is not less than 6.
5. The method according to claim 1, wherein the multi-class typical load characteristic analysis and extraction method,
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. The method according to claim 5, wherein the multi-class typical load characteristic analysis and extraction method,
and calculating the reactive power Q by adopting Fourier decomposition, and satisfying the following relation:
in the formula of UkIs an effective value of voltage, IkIs the effective value of the current, k is the harmonic frequency, and k is 0,1, 2; phi is akThe power factor angle corresponding to each harmonic frequency.
7. The method according to claim 5, wherein the multi-class typical load characteristic analysis and extraction method,
the current waveform characteristics include: root mean square IrmsAmplitude IPCrest factor IcfAnd a total harmonic distortion rate THD;
wherein, root mean square IrmsSatisfies the following relation:
wherein m is a counting subscript, N is the number of sampling points, imIs the instantaneous current at the sampling point;
wherein the amplitude value IPSatisfies the following relation:
IP＝max(im)0≤m≤N
wherein, the crest factor IcfSatisfies the following relation:
wherein, the total harmonic distortion rate THD satisfies the following relation:
wherein k is the harmonic order, IkIs the kth harmonic current, I1Is the fundamental current.
8. The method according to claim 5, wherein the multi-class typical load characteristic analysis and extraction method,
the V-I trajectory features include: the number of curve intersections, the slope of the curve centerline, and the curve closure area.
9. The method according to claim 5, wherein the multi-class typical load characteristic analysis and extraction method,
redesigning harmonic component data into multiple combinations and sequencing the multiple combinations based on a harmonic analysis algorithm of fast Fourier transform to obtain harmonic combination sequencing;
the harmonic combination order includes a direct current component, a low odd harmonic, a medium odd harmonic, a high odd harmonic, a low even harmonic, a medium even harmonic, and a high even harmonic.
10. The method according to claim 1, wherein the multi-class typical load characteristic analysis and extraction method,
in step 4, the transient characteristic data includes: transient duration, transient current step height and impact height, impact coefficient.
11. The method according to claim 10, wherein the multi-class typical load characteristic analysis and extraction method,
the transient duration Δ T satisfies the following relation:
ΔT＝Tet-Tst
the transient current step height Δ IsteadySatisfies the following relation:
ΔIsteady＝Iet-Ist
the impact height Δ IimpulseSatisfies the following relation:
ΔIimpulse＝Imax-Ist
the impact coefficient V satisfies the following relation:
in the formula, TetIs the transient start time, TstAs the end of the transient, IetIs the current value at the transient start time, IstCurrent value at the end of transient, ImaxIs the maximum current value in the transient phase.
12. The method according to claim 1, wherein the multi-class typical load characteristic analysis and extraction method,
the non-electrical characteristic data includes: the time characteristic of the intermittent operation of the load, the seasonal characteristic of the intermittent operation of the load and the associated characteristic of the intermittent operation of the load.
13. The method according to claim 12, wherein the multi-class typical load characteristic analysis and extraction method,
in step 5, extracting the time characteristic of the intermittent operation of the load, including:
step 5.1, constructing a unit time sliding window, 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-on event and a load-off event;
step 5.3, calculating the number of times of intermittent operation of the load in unit time;
step 5.4, calculating the time length of intermittent operation of the load in unit time;
and 5.5, sliding the n discrete power points to form a new sliding window, and repeating the step 5.2.
14. The method according to claim 13, wherein the multi-class typical load characteristic analysis and extraction method,
in step 5.3, the number of times of load intermittent operation in unit time is the sum of the number of times of load input events and load removal events in the sliding window divided by 2.
15. The method according to claim 13, wherein the multi-class typical load characteristic analysis and extraction method,
in step 5.4, the time length of the intermittent operation of the load in unit time is the sum of the time intervals of all load cutting events and the next load input event.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110791277.0A CN113408481A (en) | 2021-07-13 | 2021-07-13 | Multi-class typical load characteristic analysis and extraction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110791277.0A CN113408481A (en) | 2021-07-13 | 2021-07-13 | Multi-class typical load characteristic analysis and extraction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113408481A true CN113408481A (en) | 2021-09-17 |
Family
ID=77686193
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110791277.0A Pending CN113408481A (en) | 2021-07-13 | 2021-07-13 | Multi-class typical load characteristic analysis and extraction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113408481A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114629759A (en) * | 2022-03-16 | 2022-06-14 | 中国电子科技集团公司第十研究所 | Method and system for identifying fine features of aerial target communication signals |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012106709A2 (en) * | 2011-02-04 | 2012-08-09 | Myenersave, Inc. | Systems and methods for improving the accuracy of appliance level disaggregation in non-intrusive appliance load monitoring techniques |
| CN103105556A (en) * | 2013-01-30 | 2013-05-15 | 西安交通大学 | Intelligent power grid load testing and recognition method based on steady state and transient state characteristic joint matching |
| WO2019128843A1 (en) * | 2017-12-26 | 2019-07-04 | 江苏智臻能源科技有限公司 | Non-invasive ih electric cooker operating identification method based on hybrid criterion |
| CN110426554A (en) * | 2019-08-09 | 2019-11-08 | 威胜集团有限公司 | Household loads electric characteristic extracting method, device and computer readable storage medium |
| CN111244954A (en) * | 2020-03-23 | 2020-06-05 | 广东电科院能源技术有限责任公司 | Non-invasive load identification method and device |
| CN111398654A (en) * | 2020-03-06 | 2020-07-10 | 深圳供电局有限公司 | Household electric water heater identification method for residential users |
-
2021
- 2021-07-13 CN CN202110791277.0A patent/CN113408481A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012106709A2 (en) * | 2011-02-04 | 2012-08-09 | Myenersave, Inc. | Systems and methods for improving the accuracy of appliance level disaggregation in non-intrusive appliance load monitoring techniques |
| CN103105556A (en) * | 2013-01-30 | 2013-05-15 | 西安交通大学 | Intelligent power grid load testing and recognition method based on steady state and transient state characteristic joint matching |
| WO2019128843A1 (en) * | 2017-12-26 | 2019-07-04 | 江苏智臻能源科技有限公司 | Non-invasive ih electric cooker operating identification method based on hybrid criterion |
| CN110426554A (en) * | 2019-08-09 | 2019-11-08 | 威胜集团有限公司 | Household loads electric characteristic extracting method, device and computer readable storage medium |
| CN111398654A (en) * | 2020-03-06 | 2020-07-10 | 深圳供电局有限公司 | Household electric water heater identification method for residential users |
| CN111244954A (en) * | 2020-03-23 | 2020-06-05 | 广东电科院能源技术有限责任公司 | Non-invasive load identification method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114629759A (en) * | 2022-03-16 | 2022-06-14 | 中国电子科技集团公司第十研究所 | Method and system for identifying fine features of aerial target communication signals |
| CN114629759B (en) * | 2022-03-16 | 2023-05-23 | 中国电子科技集团公司第十研究所 | Method and system for identifying fine features of aerial target communication signal |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107341326B (en) | Service life evaluation method for modular multilevel converter | |
| CN108693499B (en) | User power load identification intelligent electric energy meter detection method and device | |
| CN107561354B (en) | More electric appliance non-intruding discrimination methods based on power duration characteristic | |
| CN105629065B (en) | Micro-wave oven non-intruding discrimination method based on the analysis of average active reactive integral spectrum | |
| EP2304449B1 (en) | Utility metering | |
| CN104316894B (en) | A kind of realistically displayed method of electric energy meter actual motion environment | |
| US20110144932A1 (en) | Power Monitoring and Analysis System for Identifying and Tracking Individual Electrical Devices | |
| CN105067966A (en) | Low-voltage AC arc fault detection method based on characteristic mode component energy analysis | |
| CN108152630A (en) | A kind of non-intrusion type IH electric cookers based on mixing criterion run discrimination method | |
| CN113408481A (en) | Multi-class typical load characteristic analysis and extraction method | |
| CN107179440A (en) | A kind of sub-synchronous oscillation m-Acetyl chlorophosphonazo extracting method of online adaptive frequency change | |
| CN105676028B (en) | A kind of resident load electricity consumption recognition methods based on template matches filtering | |
| Liu et al. | A new event detection technique for residential load monitoring | |
| Chakraborty et al. | Behaviour of a High Frequency Parallel Quasi Resonant Inverter Fitted Induction Heater with Different Switching Frequencies. | |
| CN111398654A (en) | Household electric water heater identification method for residential users | |
| Fahmi et al. | Harmonic reduction by using single-tuned passive filter in plastic processing industry | |
| CN114489187B (en) | Temperature control method and system for rapid temperature change test box | |
| CN110571806A (en) | Feature extraction and identification method for load category of power distribution network | |
| Heffernan et al. | Harmonic performance of heat‐pumps | |
| Zhibin et al. | Research on household appliances recognition method based on data screening of deep learning | |
| CN201550029U (en) | Digital control type power converter for kitchen ranges | |
| CN110286268A (en) | Power waveform distortion appraisal procedure based on frequency fluctuation | |
| CN203301359U (en) | Active PFC circuit | |
| Sudiarto et al. | Characteristics of Disturbance in Frequency 9-150 kHz of Photovoltaic System under Fluctuated Solar Irradiance | |
| Shi et al. | Load identification method of household smart meter based on decision tree algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |