CN110726870A - Load switch event detection method and system based on data purity - Google Patents
Load switch event detection method and system based on data purity Download PDFInfo
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Abstract
The embodiment of the invention discloses a load switch event detection method and a system based on data purity, wherein the method comprises the following steps: step 1, inputting an actually measured power signal sequence S; and 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
Description
Technical Field
The invention relates to the field of electric power, in particular to a load switch event detection method and system.
Background
With the development of smart grids, the analysis of household electrical loads becomes more and more important. Through the analysis of the power load, a family user can obtain the power consumption information of each electric appliance and a refined list of the power charge in time; the power department can obtain more detailed user power utilization information, can improve the accuracy of power utilization load prediction, and provides a basis for overall planning for the power department. Meanwhile, the power utilization behavior of the user can be obtained by utilizing the power utilization information of each electric appliance, so that the method has guiding significance for the study of household energy consumption evaluation and energy-saving strategies.
The current electric load decomposition is mainly divided into an invasive load decomposition method and a non-invasive load decomposition method. The non-invasive load decomposition method does not need to install monitoring equipment on internal electric equipment of the load, and can obtain the load information of each electric equipment only according to the total information of the electric load. The non-invasive load decomposition method has the characteristics of less investment, convenience in use and the like, so that the method is suitable for decomposing household load electricity.
In the non-invasive load decomposition algorithm, the detection of the switching event of the electrical equipment is the most important link. The initial event detection takes the change value of the active power P as the judgment basis of the event detection, and is convenient and intuitive. This is because the power consumed by any one of the electric devices changes, and the change is reflected in the total power consumed by all the electric devices. Besides the need to set a reasonable threshold for the power variation value, this method also needs to solve the problem of the event detection method in practical application: a large peak (for example, a motor starting current is much larger than a rated current) appears in an instantaneous power value at the starting time of some electric appliances, so that an electric appliance steady-state power change value is inaccurate, and the judgment of a switching event is influenced, and the peak is actually pulse noise; moreover, the transient process of different household appliances is long or short (the duration and the occurrence frequency of impulse noise are different greatly), so that the determination of the power change value becomes difficult; due to the fact that the active power changes suddenly when the quality of the electric energy changes (such as voltage drop), misjudgment is likely to happen. The intensity of (impulse) noise is large and background noise has a large impact on the correct detection of switching events.
Load switching events that are now commonly used are often determined using changes in power data: when the power change value exceeds a preset threshold value, a load switch event is considered to occur. This approach, while simple and easy to implement, results in a significant drop in the accuracy of the switching event detection due to the impulse noise and the common use of non-linear loads.
Therefore, in the switching event detection process, how to improve the switching event detection accuracy is very important. Load switch event detection is the most important step in energy decomposition, and can detect the occurrence of an event and determine the occurrence time of the event. However, the accuracy of the detection of the switching event is greatly affected by noise in the power signal (power sequence), and particularly, impulse noise generally exists in the power signal, which further affects the detection accuracy. Therefore, it is currently a very important task to effectively improve the detection accuracy of the load switch event.
Load switching events that are now commonly used are often determined using changes in power data: when the power change value exceeds a preset threshold value, a load switch event is considered to occur. This approach, while simple and easy to implement, results in a significant drop in the accuracy of the switching event detection due to the impulse noise and the common use of non-linear loads.
Disclosure of Invention
The invention aims to provide a load switch event detection method and system based on data purity. The method has good switching event detection performance and is simple in calculation.
In order to achieve the purpose, the invention provides the following scheme:
a load switch event detection method based on data purity, comprising:
step 1, inputting actually measured power signal sequenceS;
And 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
A load switch event detection system based on data purity, comprising:
an acquisition module for inputting the measured power signal sequenceS;
And the detection module detects the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
although the load switch event detection method based on power data transformation has wide application in load switch event detection and relatively mature technology, due to the wide application of the nonlinear load, the power signal is easily affected by the environmental noise and the nonlinear noise, so that the method often cannot obtain satisfactory results when applied in the actual working environment.
The invention aims to provide a load switch event detection method and system based on data purity. The method has good switching event detection performance and is simple in calculation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of a load switch event detection method based on data purity
Fig. 1 is a schematic flow chart of a load switch event detection method based on data purity according to the present invention. As shown in fig. 1, the load switch event detection method based on data purity specifically includes the following steps:
step 1, inputting actually measured power signal sequenceS;
And 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
Before the step 2, the method further comprises:
step 3, obtaining the data purity H of the Kth windowKAnd the load switch event judgment threshold e0。
The step 3 comprises the following steps:
step 301, generating the nth signal difference sequence Δ SnThe method specifically comprises the following steps:
ΔSn=[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
wherein:
ΔSi=Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S=[S1,S2,…,SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si=0。
SNR: signal-to-noise ratio of the signal sequence S
Step 302, iteratively calculating the nth signal difference sequence Δ SnThe category center of (1) is specifically:
the first step is as follows: performing iterative initialization, specifically:
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
Initialization value of first class center
k=0
Wherein:
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
wherein:
Wherein:
Step k value of the second class element set
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first classAnd a class center of the second class
Step 303, obtaining the nth signal difference sequence Δ SnThe optimal projection factor χ specifically is:
wherein:
W=[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
Step 304, calculating the data purity H of the Kth windowKThe method specifically comprises the following steps:
step 305, obtaining the operation state judgment threshold e0The method specifically comprises the following steps:
FIG. 2 structural intent of a data purity based load switch event detection system
Fig. 2 is a schematic structural diagram of a load switch event detection system based on data purity according to the present invention.
As shown in fig. 2, the load switch event detection system based on data purity includes the following structure:
an obtaining module 401 for inputting the measured power signal sequenceS;
A detection module 402 detects a load switch event based on the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
The system further comprises:
a calculating module 403, for obtaining the data purity H of the Kth windowKAnd the load switch event judgment threshold e0。
The calculation module 403 further includes the following units:
the first calculation unit 4031 generates an nth signal differential sequence Δ Sn, specifically:
ΔSn=[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
wherein:
ΔSi=Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S=[S1,S2,…,SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si=0。
SNR: signal-to-noise ratio of the signal sequence S
An iteration unit 4032 for iteratively calculating the nth signal difference sequence Δ SnThe category center of (1) is specifically:
the first step is as follows: performing iterative initialization, specifically:
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
k=0
Wherein:
Initialization value of class judgment threshold value
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
wherein:
Wherein:
Step k value of the second class element set
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first classAnd said secondClass center of classes
Second calculation unit 4033, which finds the nth signal difference sequence Δ SnThe optimal projection factor χ specifically is:
wherein:
W=[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
A third calculation unit 4034 for calculating the data purity H of the Kth windowKThe method specifically comprises the following steps:
a fourth calculation unit 4035 for obtaining the operating state determination threshold e0The method specifically comprises the following steps:
the following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
1. inputting a sequence of measured power signals
S=[s1,s2,…,sN-1,sN]
Wherein:
s: a data sequence of measured power signal of length N
siI is 1,2, …, N is the measured power signal with serial number i
2. Generating a signal difference sequence
ΔSn=[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
Wherein:
ΔSi=Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S=[S1,S2,…,SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si=0。
SNR: signal-to-noise ratio of the signal sequence S
3. Iterative determination of class centers of signal difference sequences
The first step is as follows: performing iterative initialization, specifically:
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
k=0
Wherein:
Initialization value of second category element set
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
wherein:
N1: the first categoryNumber of middle element
Step k first classClass center of
Wherein:
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first classAnd a class center of the second class
4. Finding the best projection factor
Wherein:
W=[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
5. Calculating window data purity
6. Calculating a threshold for determining the operating state
7. Detecting a switching event
Load switch events are detected based on the data purity properties. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A load switch event detection method based on data purity, comprising:
step 1, inputting an actually measured power signal sequence S;
and 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected.
Wherein e is0A threshold is determined for the load switch event.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 3, obtaining the data purity H of the Kth windowKAnd the load switch event judgment threshold e0。
3. The method of claim 2, wherein step 3 comprises:
step 301, generating the nth signal difference sequence Δ SnThe method specifically comprises the following steps:
ΔSn=[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
wherein:
ΔSi=Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S=[S1,S2,…,SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si=0。
SNR: signal-to-noise ratio of the signal sequence S
Step 302, iteratively calculating the nth signal difference sequence Δ SnThe category center of (1) is specifically:
the first step is as follows: performing iterative initialization, specifically:
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
k=0
Wherein:
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
wherein:
N2: the second classNumber of middle element
Wherein:
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first classAnd a class center of the second class
Step 303, obtaining the nth signal difference sequence Δ SnThe optimal projection factor χ specifically is:
wherein:
W=[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
Step 304, calculating the data purity H of the Kth windowKThe method specifically comprises the following steps:
step 305, obtaining the operation state judgment threshold e0The method specifically comprises the following steps:
4. a load switch event detection system based on data purity, comprising:
the acquisition module inputs an actually measured power signal sequence S;
and the detection module detects the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
5. The system of claim 4, further comprising:
a calculation module for calculating the data purity H of the Kth windowKAnd stationThe load switch event judgment threshold e0。
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