CN112180152A - Load switch event detection method and system by means of mean shift clustering - Google Patents

Load switch event detection method and system by means of mean shift clustering Download PDF

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Publication number
CN112180152A
CN112180152A CN202011068018.7A CN202011068018A CN112180152A CN 112180152 A CN112180152 A CN 112180152A CN 202011068018 A CN202011068018 A CN 202011068018A CN 112180152 A CN112180152 A CN 112180152A
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signal sequence
value
window
load switch
switch event
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翟明岳
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Guangdong University of Petrochemical Technology
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Guangdong University of Petrochemical Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R21/00—Arrangements for measuring electric power or power factor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract

The embodiment of the invention discloses a load switch event detection method and a system by using mean shift clustering, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, generating N window signal sequences; step 103, performing iterative initialization; step 104, iterative updating; step 105, ending the iteration; step 106 of obtaining the mean shift grouping value HK(ii) a Step 107 for obtaining a threshold for determining a load switch event0(ii) a Step 108 detects a load switch event.

Description

Load switch event detection method and system by means of mean shift clustering
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 means of mean shift grouping. 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 mean shift clustering, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure BDA0002714426220000021
The formula is obtained as
Figure BDA0002714426220000022
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure BDA00027144262200000210
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nmeans taking K-M + N modulo NThe remainder;
Figure BDA0002714426220000023
is a window length parameter;
Figure BDA0002714426220000024
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
step 103, performing iterative initialization, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure BDA0002714426220000025
Initialization value as offset value
Wherein:
Figure BDA0002714426220000026
for the Kth window signal sequence BKThe q-th element of (1);
step 104, iterative updating, specifically:
Figure BDA0002714426220000027
wherein: i is 1, 2M is the sequence number of the updating element;
Figure BDA0002714426220000028
for the Kth window signal sequence BKThe ith element in (1);
Figure BDA0002714426220000029
Figure BDA0002714426220000031
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure BDA0002714426220000032
for the Kth window signal sequence BKThe z-th element of (1);
Figure BDA0002714426220000033
is a probability;
m0is the mean of the signal sequence S;
xkthe k step value of the offset value;
and step 105, ending iteration, specifically: adding 1 to the value of the iteration control parameter k, and returning to the step 104 and the step 105 for iterative updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure BDA0002714426220000034
Step 106 of obtaining the mean shift grouping value HKThe method specifically comprises the following steps:
Figure BDA0002714426220000035
step 107 for obtaining a threshold for determining a load switch event0The method specifically comprises the following steps:
Figure BDA0002714426220000036
step 108, detecting a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected.
A load switch event detection system using mean shift clustering, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure BDA0002714426220000037
The formula is obtained as
Figure BDA0002714426220000038
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure BDA00027144262200000310
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nthe remainder is obtained by taking N as a modulus to K-M + N;
Figure BDA0002714426220000039
is a window length parameter;
Figure BDA0002714426220000041
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
module 203 iteratively initializes, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure BDA0002714426220000042
Initialization value as offset value
Wherein:
Figure BDA0002714426220000043
for the Kth window signal sequence BKThe q-th element of (1);
module 204 iteratively updates, specifically:
Figure BDA0002714426220000044
wherein: i is 1, 2M is the sequence number of the updating element;
Figure BDA0002714426220000045
for the Kth window signal sequence BKThe ith element in (1);
Figure BDA0002714426220000046
Figure BDA0002714426220000047
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure BDA0002714426220000048
for the Kth window signal sequence BKThe z-th element of (1);
Figure BDA0002714426220000049
is a probability;
m0is the mean of the signal sequence S;
xkthe k step value of the offset value;
the module 205 ends the iteration, specifically: adding 1 to the value of the iteration control parameter k, and returning to the module 204 and the module 205 for iteration updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure BDA00027144262200000410
Module 206 evaluates the mean shift clustering value HKThe method specifically comprises the following steps:
Figure BDA00027144262200000411
module 207 finds the load switch event decision threshold0The method specifically comprises the following steps:
Figure BDA0002714426220000051
the module 208 detects a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; 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 means of mean shift grouping. The method has good switching event detection performance and is simple in calculation.
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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 flow diagram of a method for load switch event detection using mean-shift clustering
Fig. 1 is a flow chart illustrating a load switch event detection method using mean shift clustering according to the present invention. As shown in fig. 1, the method for detecting a load switch event by mean shift clustering specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure BDA0002714426220000061
The formula is obtained as
Figure BDA0002714426220000062
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure BDA00027144262200000613
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nthe remainder is obtained by taking N as a modulus to K-M + N;
Figure BDA0002714426220000063
is a window length parameter;
Figure BDA0002714426220000064
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
step 103, performing iterative initialization, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure BDA0002714426220000065
Initialization value as offset value
Wherein:
Figure BDA0002714426220000066
for the Kth window signal sequence BKThe q-th element of (1);
step 104, iterative updating, specifically:
Figure BDA0002714426220000067
wherein: i is 1, 2M is the sequence number of the updating element;
Figure BDA0002714426220000068
for the Kth window signal sequence BKThe ith element in (1);
Figure BDA0002714426220000069
Figure BDA00027144262200000610
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure BDA00027144262200000611
for the Kth window signal sequence BKThe z-th element of (1);
Figure BDA00027144262200000612
is a probability;
m0is the mean of the signal sequence S;
xkthe k step value of the offset value;
and step 105, ending iteration, specifically: adding 1 to the value of the iteration control parameter k, and returning to the step 104 and the step 105 for iterative updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure BDA0002714426220000071
Step 106 of obtaining the mean shift grouping value HKThe method specifically comprises the following steps:
Figure BDA0002714426220000072
step 107 for obtaining a threshold for determining a load switch event0The method specifically comprises the following steps:
Figure BDA0002714426220000073
step 108, detecting a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected.
FIG. 2 structural intent of a load switch event detection system using mean shift clustering
FIG. 2 is a block diagram of a system for load switch event detection using mean shift clustering in accordance with the present invention. As shown in fig. 2, the load switch event detection system using mean shift clustering includes the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure BDA0002714426220000074
The formula is obtained as
Figure BDA0002714426220000075
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure BDA0002714426220000078
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nthe remainder is obtained by taking N as a modulus to K-M + N;
Figure BDA0002714426220000076
is a window length parameter;
Figure BDA0002714426220000077
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
module 203 iteratively initializes, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure BDA0002714426220000081
Initialization value as offset value
Wherein:
Figure BDA0002714426220000082
for the Kth window signal sequence BKThe q-th element of (1);
module 204 iteratively updates, specifically:
Figure BDA0002714426220000083
wherein: i is 1, 2M is the sequence number of the updating element;
Figure BDA0002714426220000084
for the Kth window signal sequence BKThe ith element in (1);
Figure BDA0002714426220000085
Figure BDA0002714426220000086
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure BDA0002714426220000087
for the Kth window signal sequence BKThe z-th element of (1);
Figure BDA0002714426220000088
is a probability;
m0is the mean of the signal sequence S;
xkthe k step value of the offset value;
the module 205 ends the iteration, specifically: adding 1 to the value of the iteration control parameter k, and returning to the module 204 and the module 205 for iteration updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure BDA0002714426220000089
Module 206 evaluates the mean shift clustering value HKThe method specifically comprises the following steps:
Figure BDA00027144262200000810
module 207 finds the load switch event decision threshold0The method specifically comprises the following steps:
Figure BDA00027144262200000811
the module 208 detects a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; 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 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure BDA0002714426220000091
The formula is obtained as
Figure BDA0002714426220000092
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure BDA0002714426220000093
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nthe remainder is obtained by taking N as a modulus to K-M + N;
Figure BDA0002714426220000094
is a window length parameter;
Figure BDA0002714426220000095
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
step 303, performing iterative initialization, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure BDA0002714426220000096
Initialization value as offset value
Wherein:
Figure BDA0002714426220000097
for the Kth window signal sequence BKThe q-th element of (1);
step 304, iterative updating, specifically:
Figure BDA0002714426220000098
wherein: i is 1, 2M is the sequence number of the updating element;
Figure BDA0002714426220000099
for the Kth window signal sequence BKThe ith element in (1);
Figure BDA0002714426220000101
Figure BDA0002714426220000102
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure BDA0002714426220000103
for the Kth window signal sequence BKThe z-th element of (1);
Figure BDA0002714426220000104
is a probability;
m0is the mean of the signal sequence S;
xkthe k step value of the offset value;
ending the iteration of step 305, specifically: the value of the iteration control parameter k is added by 1, and the steps are returned to the step 304 and the step 305Iteratively updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure BDA0002714426220000105
Step 306 of obtaining the mean shift grouping value HKThe method specifically comprises the following steps:
Figure BDA0002714426220000106
step 307 for obtaining a threshold for determining a load switch event0The method specifically comprises the following steps:
Figure BDA0002714426220000107
step 308, detecting a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; 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 mean shift clustering, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure FDA0002714426210000011
The formula is obtained as
Figure FDA0002714426210000012
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure FDA00027144262100000112
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nthe remainder is obtained by taking N as a modulus to K-M + N;
Figure FDA0002714426210000013
is a window length parameter;
Figure FDA0002714426210000014
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
step 103, performing iterative initialization, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure FDA0002714426210000015
Initialization value as offset value
Wherein:
Figure FDA0002714426210000016
for the Kth window signal sequence BKThe q-th element of (1);
step 104, iterative updating, specifically:
Figure FDA0002714426210000017
wherein: i is 1, 2M is the sequence number of the updating element;
Figure FDA0002714426210000018
for the Kth window signal sequence BKThe ith element in (1);
Figure FDA0002714426210000019
Figure FDA00027144262100000110
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure FDA00027144262100000113
for the Kth window signal sequence BKThe z-th element of (1);
Figure FDA00027144262100000111
is a probability;
m0is the mean of the signal sequence S;
xkthe k step value of the offset value;
and step 105, ending iteration, specifically: adding 1 to the value of the iteration control parameter k, and returning to the step 104 and the step 105 for iterative updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure FDA0002714426210000021
Step 106 of obtaining the mean shift grouping value HKThe method specifically comprises the following steps:
Figure FDA0002714426210000022
step 107 for obtaining a threshold for determining a load switch event0The method specifically comprises the following steps:
Figure FDA0002714426210000023
step 108, detecting a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected.
2. A load switch event detection system using mean shift clustering, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates N window signal sequences, specifically: the Kth window signal sequence is BKThe nth element is marked as
Figure FDA0002714426210000024
The formula is obtained as
Figure FDA0002714426210000025
Wherein:
k is 1,2, N is a window serial number;
n is 1,2, and N is the element number;
n is the length of the signal sequence S;
Figure FDA00027144262100000210
is the | K-M + n _ noncash of the signal sequence SNAn element;
|K-M+n|Nthe remainder is obtained by taking N as a modulus to K-M + N;
Figure FDA0002714426210000026
is a window length parameter;
Figure FDA0002714426210000027
rounding the upper part;
*: represents any independent variable;
σ0: the mean square error of the signal sequence S;
SNR: the signal-to-noise ratio of the signal sequence S;
module 203 iteratively initializes, specifically:
k is an iteration control parameter with an initialization value of 1
q is the window element number, and is randomly selected from 1, 2. cndot., 2M
Figure FDA0002714426210000028
Initialization value as offset value
Wherein:
Figure FDA0002714426210000029
for the Kth window signal sequence BKThe q-th element of (1);
module 204 iteratively updates, specifically:
Figure FDA0002714426210000031
wherein: i is 1, 2M is the sequence number of the updating element;
Figure FDA0002714426210000032
for the Kth window signal sequence BKThe ith element in (1);
Figure FDA0002714426210000033
Figure FDA0002714426210000034
is a probability center;
z is 1, 2M is an intermediate parameter;
mKfor the Kth window signal sequence BKThe mean value of (a);
Figure FDA0002714426210000039
for the Kth window signal sequence BKThe z-th element of (1);
Figure FDA0002714426210000035
is a probability;
m0is a stand forThe mean value of the signal sequence S;
xkthe k step value of the offset value;
the module 205 ends the iteration, specifically: adding 1 to the value of the iteration control parameter k, and returning to the module 204 and the module 205 for iteration updating until the difference between the results of two adjacent iterations satisfies | xk-xk-1If | ≦ 0.001, then the iterative control parameter K ═ K is determinedOObtaining the relative offset value of the window
Figure FDA0002714426210000036
Module 206 evaluates the mean shift clustering value HKThe method specifically comprises the following steps:
Figure FDA0002714426210000037
module 207 finds the load switch event decision threshold0The method specifically comprises the following steps:
Figure FDA0002714426210000038
the module 208 detects a load switch event, specifically:
if the mean shift clustering value H of the Kth windowK≥0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected.
CN202011068018.7A 2020-10-08 2020-10-08 Load switch event detection method and system by means of mean shift clustering Withdrawn CN112180152A (en)

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