CN112948742A - Non-household load identification method based on average load and total demand distortion rate - Google Patents

Non-household load identification method based on average load and total demand distortion rate Download PDF

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CN112948742A
CN112948742A CN202110293470.1A CN202110293470A CN112948742A CN 112948742 A CN112948742 A CN 112948742A CN 202110293470 A CN202110293470 A CN 202110293470A CN 112948742 A CN112948742 A CN 112948742A
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陈徐笛
谢岳
蔡慧
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China Jiliang University
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Abstract

The invention discloses a non-household load identification method based on average load and total demand distortion rate, which relates to the technical field of load identification, and comprises the following steps: collecting voltage and current signals of household power input ends of residents and carrying out filtering processing; calculating average conductance, average susceptance and average admittance modulus values and establishing a single-load offline feature library according to the three feature quantities; carrying out load switching event detection and steady state judgment by adopting a difference sequence of average conductance, average susceptance, average admittance modulus value and total demand distortion rate; and carrying out load decomposition and identification by using the average conductance, the average susceptance and the average admittance modulus value in steady-state operation. The average conductance, the average susceptance, the average admittance and the total demand distortion rate characteristic quantity introduced by the invention are comprehensive and contain the physical characteristics of the load, and the calculation process is simple when the load identification is realized.

Description

Non-household load identification method based on average load and total demand distortion rate
Technical Field
The invention relates to the field of household electrical load identification of residents, in particular to a non-household load identification method based on average load and total demand distortion rate.
Background
With the construction and development of global energy Internet, China initially establishes an interactive smart grid service system, the proportion of total resident household power load to power load is larger and larger, and the importance of bidirectional interactive feedback between a power supply grid and resident users in a smart grid is more and more obvious. The household electricity load of residents is identified, so that a guidance basis can be provided for reasonable electricity utilization of users, and demand side management of a power grid is facilitated.
Currently, load identification methods can be classified into two categories, home entry and non-home entry. The household load identification method needs to install a monitoring device for each household electrical appliance to acquire load data, and although the method has the advantage of simple data processing, the hardware cost is high, privacy is not provided, and the user experience is poor. The non-home-entry load identification method can achieve the purpose of load identification by only installing a monitoring device at a resident home-entry end and analyzing voltage and current data of a resident home-entry bus.
The non-household load identification method generally comprises five steps of data acquisition and processing, event detection, feature extraction, load decomposition and identification and the like. The data acquisition is to sample voltage and current waveforms, and the sampled data can be obtained through an electric energy meter generally; the data processing mainly realizes the filtering and denoising functions, and common software algorithms include median filtering, mean filtering, Gaussian filtering and the like. The event detection is an important basis of load decomposition identification, the event detection is to detect the input/removal event of the load, and the current effective value and the active power are commonly used as the load characteristic indexes of the event detection at present. The load characteristics reflect the physical characteristics of a certain aspect of the load, the proper characteristics can directly influence the operation time and accuracy of load identification, the current load characteristics are mainly divided into two types, namely transient characteristics and steady-state characteristics, the steady-state characteristics mainly comprise power, a V-I track, harmonic amplitude, phase angle and the like, and the transient characteristics comprise transient duration, impact current multiples and the like. The load decomposition and identification are carried out according to load characteristics and related algorithms, and genetic algorithms, particle swarm algorithms, chicken swarm algorithms and the like are often applied to the load decomposition and identification.
The invention provides a non-household load identification method based on average load and total demand distortion rate, which adopts three characteristic quantities of average conductance, average susceptance and average admittance modulus value representing average load to establish a characteristic library, utilizes a difference value sequence of the average conductance, average susceptance, average admittance modulus value and total demand distortion rate to detect and judge load switching events and a steady state, and simultaneously utilizes the average conductance, average susceptance and average admittance modulus value in the steady state to decompose and identify loads. Since the average conductance, the average susceptance, the average admittance modulus value and the total demand distortion rate comprise load physical characteristic information such as current subharmonic amplitude, voltage fundamental amplitude, fundamental power factor and the like, the characteristic quantities used by the method comprehensively comprise the physical characteristics of the load, and the calculation process is simple in five steps for realizing load identification.
Disclosure of Invention
The invention aims to provide a non-household load identification method based on average load and total demand distortion rate, which selects four characteristic quantities, namely average conductance, average susceptance, average admittance modulus value, total demand distortion rate and the like, which comprehensively contain load physical characteristic information, and can realize identification of household electricity load of residents through a simpler calculation process. In order to solve the above-mentioned purpose, the invention adopts the technical scheme that:
a non-household load identification method based on average load and total demand distortion rate comprises the following steps:
s0: setting Flag bit to be 0;
s1: carrying out M whole-period data acquisition on voltage and current waveforms of household electricity access ends of residents;
s2: filtering the collected current data;
s3, calculating the average conductance G, the average susceptance B, the average admittance modulus value Y and the total demand distortion TDD in each period;
s4, calculating difference value sequences delta G, delta B, delta Y and delta TDD of the average conductance G, the average susceptance B, the average admittance modulus value Y and the total demand distortion TDD in two adjacent periods;
s5: judging whether the Flag is 0, and if the Flag is 0, performing S6; if Flag ≠ 0, then proceed to S7;
s6 according to the average conductance GAnd the difference value sequences delta G, delta B and delta Y of the average susceptance B and the average admittance mode value Y are subjected to threshold value judgment, if any one characteristic quantity difference value exceeds the corresponding upper limit threshold value, the occurrence of a load switching event is judged, and the switching time t is recorded1Then, S7 is performed; if the difference values of the three characteristic quantities do not exceed the respective corresponding upper limit threshold values, judging that no load switching event occurs and returning to S1;
s7: threshold judgment is carried out on difference value sequences delta G, delta B, delta Y and delta TDD of average conductance G, average susceptance B, average admittance module value Y and total demand distortion TDD after switching occurs, if continuous D difference values of the four characteristic quantities do not exceed respective corresponding lower threshold values, switching load is judged to enter a steady state, and time t for entering the steady state is recorded2Then extracting the average conductance G, the average susceptance B and the average admittance modulus value | Y | of the first period after the steady state is entered as the characteristic value of the steady state after the load switching, and then carrying out S8; if any one characteristic quantity difference value exceeds the corresponding lower limit threshold value, judging that the switching load does not enter the steady state, setting the Flag bit Flag to be 1 and returning to S1;
s8: and (4) calculating the steady-state characteristic quantity of the new input or cut-off load characteristic value obtained in the step S6 and the characteristic quantity in the offline characteristic library by adopting a genetic algorithm, realizing load identification of the new input or cut-off load, and returning to the step S0.
The above steps are further explained in principle.
1. The expression form of the power supply voltage u (t) and the load current i (t) of the household electric end of the resident is as follows:
Figure BDA0002983359450000021
in the formula, U (T) is sine wave voltage provided by a power supply department, T is power frequency period of a power grid, U is effective value of voltage, and theta1Is the initial phase angle of voltage, IhIs the effective value of h-order current harmonic,
Figure BDA0002983359450000031
is h current harmonic phase angleThe value h is the number of harmonics, h is 1,2 … ∞;
carrying out whole-period acquisition on u (t) and i (t) to obtain sampling data u (n) and i (n) as follows:
Figure BDA0002983359450000032
wherein N is the number of sampling points in a single period, and N is 1, … and N.
2. The average conductance G, average susceptance B, average admittance modulus value | Y | and total demand distortion rate TDD are calculated as:
Figure BDA0002983359450000033
from the above formula, one can see:
Figure BDA0002983359450000034
in the formula, P is active power, Q is reactive power, S is fundamental wave apparent power, I0The rated current demand for household electricity of residents.
Using the sampling data u (n) and i (n), the average conductance G, average susceptance B, average admittance modulus | Y | and total demand distortion rate TDD can be calculated:
Figure BDA0002983359450000035
Figure BDA0002983359450000036
Figure BDA0002983359450000037
Figure BDA0002983359450000038
Figure BDA0002983359450000039
I1＝|Y|U (10)
Figure BDA00029833594500000310
wherein v (n) is u (n) hysteresis
Figure BDA00029833594500000311
The voltage of (2) sample data.
3. Before load identification, an offline feature library of household electrical loads of residents is established in advance, data acquisition and processing are carried out on each single load in a steady-state operation state according to the steps of S1, S2 and S3, average conductance G, average susceptance B, average admittance modulus value | Y | and total demand distortion rate TDD are calculated, and the offline feature library of the single load containing all loads is established, wherein the feature library comprises ZG＝[ZG1,ZG2…ZGm]，ZB＝[ZB1,ZB2…ZBm]And Z|Y|＝[Z|Y|1,Z|Y|2…Z|Y|m]And m is the total user load.
Further, in the above S2, the current sample data is filtered by using a median filtering method, where [ i (q-1), i (q +1) ] is taken as a window, q is 1, …, N, and the filtered current value i (q) is an intermediate value in the sample data [ i (q-1), i (q +1) ] before filtering.
Further, the difference sequence of the average conductance G, the average susceptance B, the average admittance modulus | Y | and the total demand distortion TDD in the above-mentioned S4 is calculated as:
Figure BDA0002983359450000041
wherein p is 1, …, M-1.
Further, in the above S6, the upper threshold of the difference between the average conductance G, the average susceptance B and the average admittance modulus | Y | is λG+、λB+And λ|Y|+，λG+、λB+And λ|Y|+G, B and 10% of the minimum value of | Y | in the offline feature library, respectively.
Further, the lower threshold values of the difference values of the average conductance G, the average susceptance B, the average admittance modulus | y | and the total demand distortion TDD in S7 are λG-、λB-、λ|Y|-And λTDD-，λG-、λB-、λ|Y|-And λTDD-G, B, | Y | and TDD minima, 1%, and 2%, respectively, in the offline feature library.
Further, the specific steps of performing load decomposition and identification according to the genetic algorithm in S8 are as follows:
firstly, configuring genetic algorithm parameters, wherein the genetic algorithm parameters comprise a population size s, a cross rate Pn, a variation rate Mr and a fitness function F, and the fitness function F has an expression as follows:
Fmin＝(HG-ZG*X)+(HB-ZB*X)+(H|Y|-Z|Y|*X) (13)
in the formula HGFor steady-state average conductance G value, HBIs a steady state average susceptance B value, H|Y|Is a steady state average admittance modulus value Y, ZG＝[ZG1,ZG2…ZGm]Is a single-motor average conductance G feature library, ZB＝[ZB1,ZB2…ZBm]Is a single-motor average susceptance B feature library, Z|Y|＝[Z|Y|1,Z|Y|2…Z|Y|m]The average admittance modulus value Y of a single motor is a characteristic library, m is the number of user loads, and X is [ X ]1,x2…xm]T，xj0 denotes appliance off state, and x j1 represents the on state of the appliance, and j 1,2 … m. Then, obtaining a fitness minimum value according to the fitness function, judging whether the fitness minimum value meets an iteration termination condition, if so, terminating the iteration, otherwise, continuing the iteration until the maximum fitness minimum value is reachedThe number of iterations.
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The drawings of the present invention are described below.
Fig. 1 is a schematic workflow diagram of the non-household load identification method based on average load and total demand distortion rate according to the present invention.
Detailed description of the preferred embodiments
Embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a non-household load identification method based on average load and total demand distortion rate, which comprises the following steps:
s0: setting Flag bit to be 0;
s1: the N takes the value 32, the M takes the value 20, carry on 20 whole cycle data acquisition to the voltage and current waveform of the household power consumer of the resident according to 32 points of every cycle, get the sampled data of the voltage u (N) and i (N), wherein N is 1,2 … 20 x 32;
s2: filtering the current sampling data by using a median filtering method, wherein [ i (q-1), i (q +1) ] is taken as a window, q is 1, …, N, and a filtered current value i (q) is an intermediate value in sampling data [ i (q-1), i (q +1) ] before filtering;
s3: calculating the average conductance G, the average susceptance B, the average admittance modulus value | Y | and the total demand distortion rate TDD in each period:
Figure BDA0002983359450000051
Figure BDA0002983359450000052
Figure BDA0002983359450000053
Figure BDA0002983359450000054
Figure BDA0002983359450000055
I1＝|Y|u (6)
Figure BDA0002983359450000056
wherein v (n) is u (n) hysteresis
Figure BDA0002983359450000057
Voltage data of (I)020A is the rated current demand of household electricity of residents, and G, B, | Y | and TDD values of 20 periods are obtained through the calculation;
in addition, before load identification, according to the steps of S1, S2 and S3, data sampling and processing are carried out on each single load under a steady-state operation state, the average conductance G, the average susceptance B, the average admittance modulus value | Y | and the total demand distortion rate TDD are calculated, and a single-load off-line feature library is established in advance;
s4, calculating difference value sequences delta G, delta B, delta Y and delta TDD of the average conductance G, the average susceptance B, the average admittance modulus value Y and the total demand distortion TDD in two adjacent periods, wherein the calculation formula is as follows:
Figure BDA0002983359450000061
wherein p is 1, …, 19;
s5: judging whether the Flag is 0, and if the Flag is 0, performing S6; if Flag ≠ 0, then proceed to S7;
s6, judging the threshold value according to the difference value sequence delta G, delta B and delta Y of the average conductance G, the average susceptance B and the average admittance modulus | Y |, if any value of delta G (p), delta G (p) and delta Y | (p) is larger than the upper threshold value lambda corresponding to the three characteristic valuesG+、λB+And λ|Y|+If yes, judging that a load switching event occurs and recording the switching time t1Then proceed with S7; if the difference values of the three characteristic quantities do not exceed the respective corresponding upper limit threshold values, judging that no load switching event occurs and returning to S1; lambda [ alpha ]G+、λB+And λ|Y|+G, B and 10% of the minimum value of | Y | in the offline feature library, respectively;
s7: carrying out threshold judgment on difference value sequences delta G, delta B, delta Y and delta TDD of average conductance G, average susceptance B, average admittance module value Y and total demand distortion TDD after switching occurs, and if 3 continuous difference values of the four characteristic quantities do not exceed respective corresponding lower threshold lambdaG-、λB-、λ|Y|-And λTDD-If yes, judging that the switching load enters the steady state, and recording the time t for entering the steady state2Then extracting the average conductance G, the average susceptance B and the average admittance modulus value | Y | of the first period after the steady state is entered as the characteristic value of the steady state after the load switching, and then carrying out S8; if any one characteristic quantity difference value exceeds the corresponding lower limit threshold value, judging that the switching load does not enter the steady state, setting the Flag bit Flag to be 1 and returning to S1; lambda [ alpha ]G-、λB-、λ|Y|-And λTDD-G, B, 1%, and 2% of the minimum of | Y | in the offline feature library, respectively;
s8: and (3) carrying out load decomposition and identification on the steady-state characteristic quantity and the characteristic quantity in the offline characteristic library by adopting a genetic algorithm, returning to S0, wherein the specific steps of load decomposition and identification are as follows:
firstly, configuring genetic algorithm parameters, wherein the genetic algorithm parameters comprise: the population size s is 1000, the crossing rate Pn is 0.5, the variation rate Mr is 0.1, and a fitness function F, where the fitness function F has the expression:
Fmin＝(HG-ZG*X)+(HB-ZB*X)+(H|Y|-Z|Y|*X) (9)
in the formula, HGFor steady-state average conductance G value, HBIs a steady state average susceptance B value, H|Y|Is a steady state average admittance modulus value Y, ZG＝[ZG1,ZG2…ZGm]Is a single-motor average conductance G feature library, ZB＝[ZB1,ZB2…ZBm]Is a single-motor average susceptance B feature library, Z|Y|＝[Z|Y|1,Z|Y|2…Z|Y|m]The method is characterized in that the method is a single-motor average admittance modulus | Y | feature library, wherein m is 12 is the number of user loads, and X is [ X ]1,x2…xm]T，x j0 denotes appliance off state, and xj1 represents the on state of the appliance, and j 1,2 … m. And then, obtaining a fitness minimum value according to the fitness function, judging whether the fitness minimum value meets an iteration termination condition, if so, terminating the iteration, otherwise, continuing the iteration until the maximum iteration times is reached.
In summary, the present embodiment is a non-household load identification method based on an average load and a total demand distortion rate. Compared with the selection and calculation of the characteristic quantity in the existing load identification research, the method introduces the average conductance, the average susceptance and average admittance module values representing the average load and the total demand distortion rate as the characteristic quantity, can more comprehensively contain the physical characteristic information of the load and simplifies the calculation.
The above contents are only examples of processes for embodying the present invention, and do not limit the present invention at all, and all technical solutions that fall under the idea of the present invention fall within the protection scope of the present invention.

Claims (5)

1. A non-intrusive load identification method based on average load and total demand distortion rate, characterized by: the method comprises the following steps:
s0: setting Flag bit to be 0;
s1: carrying out M whole-period data acquisition on voltage and current waveforms of household electricity access ends of residents;
s2: filtering the collected current data;
s3: calculating the average conductance G, the average susceptance B, the average admittance modulus value | Y | and the total demand distortion rate TDD in each period;
s4: calculating difference value sequences delta G, delta B, delta Y and delta TDD of the average conductance G, the average susceptance B, the average admittance modulus value Y and the total demand distortion rate TDD of two adjacent periods;
s5: judging whether the Flag is 0, and if the Flag is 0, performing S6; if Flag ≠ 0, then proceed to S7;
s6: threshold judgment is carried out according to difference value sequences delta G, delta B and delta Y of the average conductance G, the average susceptance B and the average admittance modulus Y, if any one characteristic quantity difference value exceeds the corresponding upper limit threshold value, a load switching event is judged to occur, and switching time t is recorded1Then, S7 is performed; if the difference values of the three characteristic quantities do not exceed the respective corresponding upper limit threshold values, judging that no load switching event occurs and returning to S1;
s7: threshold judgment is carried out on difference value sequences delta G, delta B, delta Y and delta TDD of average conductance G, average susceptance B, average admittance module value Y and total demand distortion TDD after switching occurs, if continuous D difference values of the four characteristic quantities do not exceed respective corresponding lower threshold values, switching load is judged to enter a steady state, and time t for entering the steady state is recorded2Then extracting the average conductance G, the average susceptance B and the average admittance modulus value | Y | of the first period after the steady state is entered as the characteristic value of the steady state after the load switching, and then carrying out S8; if any one characteristic quantity difference value exceeds the corresponding lower limit threshold value, judging that the switching load does not enter the steady state, setting the Flag bit Flag to be 1 and returning to S1;
s8: and (4) calculating the steady-state characteristic quantity of the new input or cut-off load characteristic value obtained in the step S6 and the characteristic quantity in the offline characteristic library by adopting a genetic algorithm, realizing load identification of the new input or cut-off load, and returning to the step S0.
2. The mean load and total demand distortion rate-based non-home load identification method of claim 1, wherein the mean conductance G, mean susceptance B, mean admittance modulus value | Y | and total demand distortion rate TDD are calculated in S4 as:
Figure FDA0002983359440000011
in the formula, P is active power, Q is reactive power, S is fundamental wave apparent power, I0For household electricity consumption of residentsRated current demand.
3. The method according to claim 1, wherein the threshold values Δ G, Δ B, and Δ | Y | of the difference sequence between the average conductance G, the average susceptance B, and the average admittance modulus | Y | for determining whether there is a load switching event in S5 are 10% of the minimum values of the average conductance G, the average susceptance B, and the average admittance modulus | Y | in the offline feature library, respectively.
4. The method according to claim 1, wherein the difference thresholds Δ G, Δ B, Δ | Y | and ATDD for the average conductance G, average susceptance B, average admittance mode | Y | and total demand distortion rate TDD determined in S7 are 1%, 1% and 2% of the minimum values of the average conductance G, average susceptance B, average admittance mode | Y | and total demand distortion rate TDD in the offline feature library, respectively.
5. The method for identifying non-household loads based on average loads and total demand distortion rates according to claim 1, wherein the parameters of the genetic algorithm in the step S8 include a population size S, a cross rate Pn, a variation rate Mr, and a fitness function F, and the fitness function F has an expression as follows:
Fmin＝(HG-ZG*X)+(HB-ZB*X)+(H|Y|-Z|Y|*X) (2)
in the formula, HGFor steady-state average conductance G value, HBIs a steady state average susceptance B value, H|Y|Is a steady state average admittance modulus value Y, ZG＝[ZG1，ZG2...ZGm]Is a single-motor average conductance G feature library, ZB＝[ZB1，ZB2...ZBm]Is a single-motor average susceptance B feature library, Z|Y|＝[Z|Y|1，Z|Y|2...Z|Y|m]The average admittance modulus value Y of a single motor is a characteristic library, m is the number of user loads, and X is [ X ]1，x2...xm]T，rj0 denotes appliance off state, and xj1 denotes the on state of the appliance, and j 1, 2.. m.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113255236A (en) * 2021-07-07 2021-08-13 浙江大学 Non-invasive load self-adaptive identification method based on twin network

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113255236A (en) * 2021-07-07 2021-08-13 浙江大学 Non-invasive load self-adaptive identification method based on twin network
CN113255236B (en) * 2021-07-07 2021-10-08 浙江大学 Non-invasive load self-adaptive identification method based on twin network

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