CN112329637A - Load switch event detection method and system by using mode characteristics - Google Patents

Load switch event detection method and system by using mode characteristics Download PDF

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CN112329637A
CN112329637A CN202011229086.7A CN202011229086A CN112329637A CN 112329637 A CN112329637 A CN 112329637A CN 202011229086 A CN202011229086 A CN 202011229086A CN 112329637 A CN112329637 A CN 112329637A
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CN112329637B (en
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翟明岳
李道格
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North China Electric Power University
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Abstract

The embodiment of the invention discloses a load switch event detection method and a system by using mode characteristics, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, obtaining a delay signal vector; step 103, solving a first parameter of sigma; 104, solving a second sigma parameter; step 105, obtaining a third parameter of the sigma; step 106, solving a mode characteristic solution; step 107, obtaining a window judgment value; step 108, obtaining a state judgment threshold value; step 109 determines a load switch event.

Description

Load switch event detection method and system by using mode characteristics
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.
Disclosure of Invention
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.
The invention aims to provide a load switch event detection method and system by using mode characteristics. 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 method of load switch event detection using a pattern feature, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure BDA0002764569520000021
wherein:
Figure BDA0002764569520000022
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nis shown inN is the remainder of the modulo pair k +1,
Figure BDA0002764569520000023
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure BDA0002764569520000024
indicating the | k + N-NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, N is a delay number,
n is the length of the signal sequence S;
step 103, obtaining a first sigma parameter, specifically:
the kth sigma first element is noted as
Figure BDA0002764569520000025
The solving formula is as follows:
Figure BDA0002764569520000026
wherein:
Figure BDA0002764569520000027
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure BDA0002764569520000028
for the k-th delayed signal vector dk(ii) k +2 ndNThe number of the elements is one,
Figure BDA0002764569520000029
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
step 104, obtaining a second sigma parameter, specifically:
the kth sigma second element is noted as
Figure BDA00027645695200000210
The solving formula is as follows:
Figure BDA00027645695200000211
step 105, obtaining a third sigma parameter, specifically:
the kth sigma third element is noted as
Figure BDA0002764569520000031
The solving formula is as follows:
Figure BDA0002764569520000032
step 106, obtaining a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure BDA0002764569520000033
And
Figure BDA0002764569520000034
the calculation formula used is:
Figure BDA0002764569520000035
Figure BDA0002764569520000036
step 107, obtaining a window judgment value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure BDA0002764569520000037
step 108, obtaining a state judgment threshold, specifically: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure BDA0002764569520000038
wherein:
Figure BDA0002764569520000039
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
step 109, judging a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
A load switch event detection system utilizing a mode signature, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure BDA00027645695200000310
wherein:
Figure BDA00027645695200000311
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nindicating that the remainder is modulo N for k +1,
Figure BDA0002764569520000041
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure BDA0002764569520000042
indicating the | k + N-NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, N is a delay number,
n is the length of the signal sequence S;
the module 203 calculates a first sigma parameter, which specifically includes:
the kth sigma first element is noted as
Figure BDA0002764569520000043
The solving formula is as follows:
Figure BDA0002764569520000044
wherein:
Figure BDA0002764569520000045
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure BDA0002764569520000046
for the k-th delayed signal vector dk(ii) k +2 ndNThe number of the elements is one,
Figure BDA0002764569520000047
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
the module 204 calculates a second sigma parameter, which specifically includes:
the kth sigma second element is noted as
Figure BDA0002764569520000048
The solving formula is as follows:
Figure BDA0002764569520000049
the module 205 calculates a third sigma parameter, which specifically includes:
the kth sigma third element is noted as
Figure BDA00027645695200000410
The solving formula is as follows:
Figure BDA00027645695200000411
the module 206 finds a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure BDA00027645695200000412
And
Figure BDA00027645695200000413
the calculation formula used is:
Figure BDA00027645695200000414
Figure BDA00027645695200000415
the module 207 calculates a window determination value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure BDA0002764569520000051
the module 208 calculates a state determination threshold, specifically: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure BDA0002764569520000052
wherein:
Figure BDA0002764569520000053
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
the module 209 determines a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
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.
The invention aims to provide a load switch event detection method and system by using mode characteristics. 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 flow chart 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 using pattern features
Fig. 1 is a flow chart illustrating a load switch event detection method using a mode characteristic according to the present invention. As shown in fig. 1, the method for detecting a load switch event using a mode characteristic specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure BDA00027645695200000611
wherein:
Figure BDA0002764569520000061
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nindicating that the remainder is modulo N for k +1,
Figure BDA00027645695200000612
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure BDA00027645695200000613
indicating the | k + N-NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, N is a delay number,
n is the length of the signal sequence S;
step 103, obtaining a first sigma parameter, specifically:
the kth sigma first element is noted as
Figure BDA0002764569520000062
The solving formula is as follows:
Figure BDA0002764569520000063
wherein:
Figure BDA0002764569520000064
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure BDA0002764569520000065
for the k-th delayed signalVector dk(ii) k +2 ndNThe number of the elements is one,
Figure BDA0002764569520000066
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
step 104, obtaining a second sigma parameter, specifically:
the kth sigma second element is noted as
Figure BDA0002764569520000067
The solving formula is as follows:
Figure BDA0002764569520000068
step 105, obtaining a third sigma parameter, specifically:
the kth sigma third element is noted as
Figure BDA0002764569520000069
The solving formula is as follows:
Figure BDA00027645695200000610
step 106, obtaining a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure BDA0002764569520000071
And
Figure BDA0002764569520000072
the calculation formula used is:
Figure BDA0002764569520000073
Figure BDA0002764569520000074
step 107, obtaining a window judgment value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure BDA0002764569520000075
step 108, obtaining a state judgment threshold, specifically: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure BDA0002764569520000076
wherein:
Figure BDA0002764569520000077
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
step 109, judging a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
FIG. 2 is a schematic diagram of a load switch event detection system using mode features
Fig. 2 is a schematic diagram of a load switch event detection system utilizing a mode feature of the present invention. As shown in fig. 2, the load switch event detection system using the mode feature includes the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure BDA0002764569520000078
wherein:
Figure BDA0002764569520000079
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nindicating that the remainder is modulo N for k +1,
Figure BDA00027645695200000710
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure BDA0002764569520000081
indicating the | k + N-NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, N is a delay number,
n is the length of the signal sequence S;
the module 203 calculates a first sigma parameter, which specifically includes:
the kth sigma first element is noted as
Figure BDA0002764569520000082
The solving formula is as follows:
Figure BDA0002764569520000083
wherein:
Figure BDA0002764569520000084
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure BDA0002764569520000085
for the k-th delayed signal vector dk(ii) k +2 ndNThe number of the elements is one,
Figure BDA0002764569520000086
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
the module 204 calculates a second sigma parameter, which specifically includes:
the kth sigma second element is noted as
Figure BDA0002764569520000087
The solving formula is as follows:
Figure BDA00027645695200000815
the module 205 calculates a third sigma parameter, which specifically includes:
the kth sigma third element is noted as
Figure BDA0002764569520000088
The solving formula is as follows:
Figure BDA0002764569520000089
the module 206 finds a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure BDA00027645695200000810
And
Figure BDA00027645695200000811
the calculation formula used is:
Figure BDA00027645695200000812
Figure BDA00027645695200000813
the module 207 calculates a window determination value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure BDA00027645695200000814
the module 208 calculates a state determination threshold, specifically: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure BDA0002764569520000091
wherein:
Figure BDA0002764569520000092
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
the module 209 determines a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
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:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302, obtaining a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure BDA0002764569520000093
wherein:
Figure BDA0002764569520000094
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nindicating that the remainder is modulo N for k +1,
Figure BDA0002764569520000095
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure BDA0002764569520000096
indicating the | k + N-NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, N is a delay number,
n is the length of the signal sequence S;
step 303, obtaining a first sigma parameter, specifically:
the kth sigma first element is noted as
Figure BDA0002764569520000097
The solving formula is as follows:
Figure BDA0002764569520000098
wherein:
Figure BDA0002764569520000099
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure BDA00027645695200000910
for the k-th delayed signal vector dk(ii) k +2 ndNThe number of the elements is one,
Figure BDA00027645695200000911
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
step 304, obtaining a second sigma parameter, specifically:
the kth sigma second element is noted as
Figure BDA0002764569520000101
The solving formula is as follows:
Figure BDA0002764569520000102
step 305, obtaining a third sigma parameter, specifically:
the kth sigma third element is noted as
Figure BDA0002764569520000103
The solving formula is as follows:
Figure BDA0002764569520000104
step 306, solving a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure BDA0002764569520000105
And
Figure BDA00027645695200001011
the calculation formula used is:
Figure BDA0002764569520000106
Figure BDA0002764569520000107
step 307, obtaining a window judgment value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure BDA0002764569520000108
step 308, obtaining a state judgment threshold specifically as follows: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure BDA0002764569520000109
wherein:
Figure BDA00027645695200001010
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
step 309, determining a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
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 (2)

1. A method for load switch event detection using a pattern feature, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure FDA0002764569510000011
wherein:
Figure FDA0002764569510000012
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nindicating that the remainder is modulo N for k +1,
Figure FDA00027645695100000113
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure FDA0002764569510000013
indicating the | k + N-NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, …, N is a delay sequence number,
n is the length of the signal sequence S;
step 103, obtaining a first sigma parameter, specifically:
the kth sigma first element is noted as
Figure FDA0002764569510000014
The solving formula is as follows:
Figure FDA0002764569510000015
wherein:
Figure FDA0002764569510000016
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure FDA0002764569510000017
for the k-th delayed signal vector dk(ii) k +2 ndNThe number of the elements is one,
Figure FDA0002764569510000018
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
step 104, obtaining a second sigma parameter, specifically:
the kth sigma second element is noted as
Figure FDA0002764569510000019
The solving formula is as follows:
Figure FDA00027645695100000110
step 105, obtaining a third sigma parameter, specifically:
the kth sigmaThree elements are marked as
Figure FDA00027645695100000111
The solving formula is as follows:
Figure FDA00027645695100000112
step 106, obtaining a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure FDA0002764569510000021
And
Figure FDA0002764569510000022
the calculation formula used is:
Figure FDA0002764569510000023
Figure FDA0002764569510000024
step 107, obtaining a window judgment value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure FDA0002764569510000025
step 108, obtaining a state judgment threshold, specifically: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure FDA0002764569510000026
wherein:
Figure FDA0002764569510000027
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
step 109, judging a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
2. A load switch event detection system utilizing a mode feature, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a delay signal vector, specifically: the kth delayed signal vector is denoted as dkThe formula used is:
Figure FDA0002764569510000028
wherein:
Figure FDA0002764569510000029
indicating the | k +1 < th > of the signal sequence SNThe number of the elements is one,
|k+1|Nindicating that the remainder is modulo N for k +1,
Figure FDA00027645695100000210
indicates the | k +2 < th > of the signal sequence SNThe number of the elements is one,
|k+2|Nmeaning that the remainder is modulo N for k +2,
Figure FDA00027645695100000211
representing the second of the signal sequence Sk+N|NThe number of the elements is one,
|k+N|Nmeaning that the remainder is modulo N for k + N,
k is 1,2, …, N is a delay sequence number,
n is the length of the signal sequence S;
the module 203 calculates a first sigma parameter, which specifically includes:
the kth sigma first element is noted as
Figure FDA0002764569510000031
The solving formula is as follows:
Figure FDA0002764569510000032
wherein:
Figure FDA0002764569510000033
for the k-th delayed signal vector dkThe | k +1 |)NThe number of the elements is one,
Figure FDA0002764569510000034
for the k-th delayed signal vector dk(ii) k +2 ndNThe number of the elements is one,
Figure FDA0002764569510000035
for the (k + 1) th delayed signal vector dk+1The | k +1 |)NAn element;
the module 204 calculates a second sigma parameter, which specifically includes:
the kth sigma second element is noted as
Figure FDA0002764569510000036
The solving formula is as follows:
Figure FDA0002764569510000037
the module 205 calculates a third sigma parameter, which specifically includes:
the kth sigma third element is noted as
Figure FDA0002764569510000038
The solving formula is as follows:
Figure FDA0002764569510000039
the module 206 finds a pattern feature solution, specifically:
the kth pair of mode features is solved as
Figure FDA00027645695100000310
And
Figure FDA00027645695100000311
the calculation formula used is:
Figure FDA00027645695100000312
Figure FDA00027645695100000313
the module 207 calculates a window determination value, specifically: the k-th window judgment value is recorded as HkThe formula used is:
Figure FDA00027645695100000314
the module 208 calculates a state determination threshold, specifically: the state judgment threshold is marked as epsilon, and the solving formula is as follows:
Figure FDA00027645695100000315
wherein:
Figure FDA00027645695100000316
is prepared from the raw materials of Frobenus die,
||dk||Ffor the k-th delayed signal vector dkThe Frobenus moustache of (1);
the module 209 determines a load switch event, specifically: if the k window judges the value HkSatisfies the judgment condition | HkIf | ≧ epsilon, at the kth point of the signal sequence S, a load switch event is detected; otherwise, no load switch event is detected.
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