CN110906434A - Non-invasive electric heater load identification method and system - Google Patents

Non-invasive electric heater load identification method and system Download PDF

Info

Publication number
CN110906434A
CN110906434A CN201811091298.6A CN201811091298A CN110906434A CN 110906434 A CN110906434 A CN 110906434A CN 201811091298 A CN201811091298 A CN 201811091298A CN 110906434 A CN110906434 A CN 110906434A
Authority
CN
China
Prior art keywords
clustering
fuzzy
characteristic value
electric heater
optimal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201811091298.6A
Other languages
Chinese (zh)
Inventor
张新鹤
蒋利民
黄伟
屈博
成岭
李克成
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Original Assignee
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, China Electric Power Research Institute Co Ltd CEPRI filed Critical State Grid Corp of China SGCC
Priority to CN201811091298.6A priority Critical patent/CN110906434A/en
Publication of CN110906434A publication Critical patent/CN110906434A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D19/00—Details
    • F24D19/10—Arrangement or mounting of control or safety devices
    • F24D19/1096—Arrangement or mounting of control or safety devices for electric heating systems
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D2200/00—Heat sources or energy sources
    • F24D2200/08—Electric heater

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Thermal Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a method for calculating active power and reactive power according to power consumption information acquired in a time period to be measured; taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result; and comparing each type of data in the optimal clustering result with the characteristics of the electric appliances, and selecting the most similar data as the characteristic value of electric heater load identification. The problems that the number of electric equipment in a monitored system is large, the hardware cost is high, and the installation, the system maintenance and the management are inconvenient are solved, so that the investment cost and the operation cost of the whole monitoring system of the detection method are low, and the method is easy to popularize.

Description

Non-invasive electric heater load identification method and system
Technical Field
The invention relates to the technical field of power load monitoring, in particular to a non-invasive electric heater load identification method and system.
Background
The civilization of human society can not be developed from ancient times, and the energy is vital to the competition of national strategy and citizens. At present, with the advancement of urbanization and the adjustment of economic structures in China, the electricity consumption of residents is rapidly increased in an exponential mode, the loads of residents are used as important components of power loads, and the specific consumption of electric energy is gradually increased, so that the energy conservation and the energy conservation of residents play an important role in promoting the energy conservation and emission reduction of the whole society and relieving the energy crisis and climate change, and the monitoring of the power loads has very important significance for the planning and the operation of power systems of power consumers and power companies. The load monitoring can provide more detailed electric energy use data of different levels, which is beneficial to reducing the electric energy consumption of resident users and reducing the cost of electric charges; and the method is also helpful for the power company to standardize the investment of the power system and reduce the running loss of the power system. In a word, a more accurate and more reliable scientific basis can be provided for improving the power load prediction precision.
User side load monitoring is mainly intrusive. The intrusive load monitoring method needs to install the power consumption information acquisition device for each electric device to obtain the electric energy data of the load, when the number of the electric devices in the monitored system is large, the hardware cost is high, and the installation, the system maintenance and the management are inconvenient.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a non-invasive electric heater load identification method.
The technical scheme provided by the invention is as follows:
a non-intrusive electric heater load identification method comprises the following steps:
calculating active power and reactive power according to the power consumption information acquired in the time period to be measured;
taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result;
and comparing each type of data in the optimal clustering result with the characteristics of the electric appliances, and selecting the most similar data as the characteristic value of electric heater load identification.
Preferably, the electricity consumption information collected in the time period to be measured includes: and sampling data of voltage and current in a time period to be monitored by a user.
Preferably, the active power is calculated according to the following formula;
Figure BDA0001803471830000021
in the formula of UkIs the maximum voltage; i iskIs the maximum value of the current;
Figure BDA0001803471830000022
is the phase difference; p represents active power; k: is the harmonic order;
the reactive power is calculated as follows:
Figure BDA0001803471830000023
in the formula, Q represents reactive power.
Preferably, the fuzzy C-value clustering includes:
obtaining the membership degree of each sample point to the belonged class center through a clustering objective function, grouping the sample points, and solving the clustering center of each group;
performing iterative computation on the clustering target function to enable the target function to obtain a minimum value;
and when the target function obtains the minimum value, obtaining the optimal clustering result according to a Lagrange multiplier method.
Preferably, the clustering objective function is shown as the following formula;
Figure BDA0001803471830000024
wherein J is a clusterAn objective function; u ═ Uij)，uijE (0, 1) is a fuzzy membership matrix, and the sum of the membership degrees of the normalized data set is always equal to 1; c. CiCluster centers for fuzzy group i; m is a weighted index of the membership matrix, used for controlling the fuzzy degree of U, and belongs to [1, ∞ ]; dijAnd the distance between the ith clustering center and the jth data point is shown, wherein the clustering centers comprise no load, small load and large load.
Preferably, when the objective function obtains the minimum value, obtaining the optimal clustering result according to the lagrangian multiplier method includes:
when the target function obtains the minimum value, obtaining the optimal clustering center and the optimal fuzzy membership degree according to a Lagrange multiplier method; the optimal fuzzy membership is calculated through an optimal fuzzy membership matrix;
wherein the optimal clustering center is calculated according to the following formula:
Figure BDA0001803471830000031
the optimal fuzzy membership matrix is shown as follows:
Figure BDA0001803471830000032
preferably, the comparing, based on each type of data in the optimal clustering result and the electrical appliance characteristics, the selecting the most similar data as the characteristic value of the electric heater load identification includes:
respectively calculating the closeness of the actually measured characteristic value and each cluster center in the optimal clustering result;
when the closeness of the actually measured characteristic value and the clustering center meets a first judgment formula, the actually measured characteristic value is closest to the clustering center, and the actually measured characteristic value is subordinate to the cluster corresponding to the clustering center;
and taking the class to which the actually measured characteristic value belongs as the characteristic value for identifying the load of the electric heater.
Preferably, the closeness is calculated according to the following formula:
Figure BDA0001803471830000033
in the above formula, N is closeness; mu.sD(U) is the membership of element U to D in the universe of discourse U; mu.sVAnd (U) is the degree of membership of the element U to V in the universe of discourse U.
Preferably, the first judgment formula is as follows:
Figure BDA0001803471830000034
wherein N is closeness; d is calculating the characteristic value of actual measurement; v is the cluster center.
A non-intrusive electric heater load identification system, comprising: the device comprises a calculation module, a determination module and a selection module:
the calculation module is used for calculating active power and reactive power according to the power utilization information acquired in the time period to be measured;
the determining module is used for taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result;
and the selection module is used for comparing each type of data in the optimal clustering result with the characteristics of the electric appliances and selecting the most similar data as the characteristic value of electric heater load identification.
Preferably, the calculation module includes: a first computation submodule and a second computation submodule;
the active power of the first computing submodule is computed according to the following formula:
Figure BDA0001803471830000041
in the formula of UkIs the maximum voltage; i iskIs the maximum value of the current;
Figure BDA0001803471830000042
is the phase difference;p represents active power; k: is the harmonic order;
the reactive power of the second calculation submodule is calculated according to the following formula:
Figure BDA0001803471830000043
in the formula, Q represents reactive power.
Preferably, the determining module includes: a function submodule and a determination submodule;
the objective function of the function sub-module cluster is shown as follows:
Figure BDA0001803471830000044
in the formula, J is a clustering objective function; u ═ Uij)，uijE (0, 1) is a fuzzy membership matrix, the sum of membership degrees of the normalized data set is always equal to 1, ciFor the clustering center of the fuzzy group i, m ∈ [ l, ∞) m is a weighted index of the membership matrix, which is used to control the fuzzy degree of U, dijThe distance between the ith clustering center and the jth data point is defined, wherein the clustering center comprises no load, small load and large load;
the determining submodule is used for enabling the target function to obtain the minimum value through iterative calculation, and obtaining the clustering center and the fuzzy membership matrix of the fuzzy group i according to a Lagrange multiplier method.
Preferably, the selection module includes: a calculation submodule and a selection submodule;
the calculation submodule is used for calculating the closeness of the actually measured characteristic value and each clustering center in the optimal clustering result;
the selection submodule is used for enabling the actually measured characteristic value to be closest to the clustering center when the closeness of the actually measured characteristic value and the clustering center meets a first judgment formula, and enabling the actually measured characteristic value to belong to the cluster corresponding to the clustering center; and taking the class to which the actually measured characteristic value belongs as the characteristic value for identifying the load of the electric heater.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a method for calculating active power and reactive power according to power consumption information acquired in a time period to be measured; taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result; and comparing each type of data in the optimal clustering result with the characteristics of the electric appliances, and selecting the most similar data as the characteristic value of electric heater load identification. The problems of large quantity of electric equipment in a monitored system and high hardware cost are solved, the investment cost and the operation cost of the whole monitoring system of the detection method are reduced, and the method is easy to popularize.
2. Active power and reactive power are extracted as characteristics to serve as characterization quantities of different load states, and the characterization quantities are used as input quantities of a fuzzy C-means clustering algorithm to identify the different load states. The fuzzy C-means clustering is applied to the identification of non-invasive electric heater load monitoring, and the method is simple and effective.
Drawings
FIG. 1 is a flow chart of a non-intrusive electric heater load identification method;
fig. 2 is a flowchart of an embodiment of a non-intrusive electric heater load identification method.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1
As shown in fig. 2, a non-intrusive electric heater load identification method includes the following steps:
step (1): and collecting voltage and current sampling data in a time period to be monitored of a user.
Step (2): and calculating the characteristic values of active power, reactive power and harmonic wave according to the sampling data.
And (3): according to different electric quantity parameters when different loads are started, active power and reactive power are used as data input of fuzzy C value clustering.
And (4): and calculating the clustering result, and continuously iterating to obtain the optimal clustering number.
And (5): and according to the similarity between each type of data and the characteristics of the electric appliances, selecting the most similar data as the characteristic value of the electric heater to finish the identification of the load of the electric heater.
Preferably, in step (1):
voltage and current data in a time period to be monitored are acquired through a smart meter of each power consumer and are recorded as u (i), i (i), and i (i), wherein i is 1, 2, 3, …, and n, wherein u (i), i (i) and i (i) represent the voltage and current values of the ith sampling point, n represents the number of the sampling points, ti represents the sampling time of the ith sampling point, and △ t is ti-ti-1 as a sampling interval.
Preferably, in step (2):
calculating to obtain complex power according to the voltage and current data acquired in the step (1) and respective phase angles,
Figure BDA0001803471830000061
here, Um and Im are maximum values of voltage and current, respectively, and θ u and θ i are phase angles of voltage and current, respectively.
Figure BDA0001803471830000062
P denotes active power Q denotes reactive power.
Preferably, in step (3):
taking active power and reactive power as load characteristics;
further according to the formula
Figure BDA0001803471830000063
P, Q, V, I are the active power, reactive power, voltage and current of different electrical appliances; the phase difference of the voltage and the current of different electric appliances is shown in the middle; k is the harmonic order.
Further, power characteristics are calculated through voltage and current obtained through sampling, and active power and reactive power are adopted as data input of fuzzy C value clustering according to different electric quantity parameters when different loads are started.
Further, the fuzzy C-means clustering (FCM) algorithm is widely applied, and the membership degree of each sample point to all class centers is obtained by optimizing an objective function. The load states can be classified into three types, i.e., no load, small load and large load, each load state can be represented by a vector X, where X is (X1a, X2a), X1a, and X2a respectively represent active power and reactive power (xla, X2a are the aforementioned P, Q).
Further, the data are divided into c fuzzy groups according to the load state, and the clustering center of each group is solved to minimize the objective function of the non-similarity index. FCM clustering allows each given data point to determine its degree of belonging to each group by using a degree of membership between 0 and 1, and the membership matrix U allows elements with values between 0 and 1. However, plus the normalization provision, the sum of the membership of one dataset is always equal to 1:
Figure BDA0001803471830000071
the objective function of FCM clustering is generally of the form:
Figure BDA0001803471830000072
wherein the fuzzy membership matrix U ═ U (U)ij)，uijE (0, 1), the sum of membership of the normalized data set is always equal to 1, ci is the clustering center of the fuzzy group i, m e [1, ∞ ] is a weighted index of the membership matrix for controlling the fuzzy degree of U, dij is the Euclidean distance between the ith clustering center and the jth data point, and is defined as: dij＝||ci-xj||＝(ci-xj)TB(ci-xj)。
Preferably, in step (4):
and (4) enabling the objective function to obtain the minimum value through iterative calculation. When the objective function takes the minimum value, it can be obtained according to the Lagrange multiplier method:
Figure BDA0001803471830000073
in the formula: c. CiIs the i-th class center. The fuzzy clustering algorithm is to achieve the final clustering purpose by repeatedly modifying the membership degree matrix and the clustering center.
Preferably, in step (5):
and (4) after the fuzzy clustering iteration in the step (4) is carried out to obtain a clustering center, calculating the closeness of the actually measured characteristic value and the clustering center, and thus determining which load belongs to.
Further, given a fuzzy subset V and another fuzzy subset D on the domain of discourse U, the maximum minimum closeness N (D, V) is defined as:
Figure BDA0001803471830000074
in the above formula, μ D (U) and μ V (U) are membership degrees of the element U in the domain of discourse to D and V;
further, given the fuzzy subsets V1, V2, … Vn and another fuzzy subset D on the domain of discourse U, if 1 ≦ i ≦ n, the equation:
Figure BDA0001803471830000075
then D is considered closest to Vi and should belong to that class.
And according to the similarity between each type of data and the characteristics of the electric appliances, selecting the most similar data as the characteristic value of the electric heater to finish the identification of the load of the electric heater.
Example 2
The data acquisition is carried out under a 50Hz power system, voltage and current data in a time period to be monitored are acquired through an intelligent ammeter of each power consumer, the acquisition frequency is 1kHz, the monitoring time is 10min, the sampling time is acquired once every 6s, and the voltage and the current of each time point are recorded, so that the active power and the reactive power are calculated. And inputting the calculated active power and reactive power as fuzzy c clustering, continuously iterating the calculated clustering result to obtain the optimal clustering number, and selecting the most similar data as the characteristic value of the electric heater according to the similarity of each type of data and the characteristics of the electric appliance to finish the identification of the electric heater load.
Example 3
As shown in fig. 1, a non-intrusive electric heater load identification method includes:
calculating active power and reactive power according to the power consumption information acquired in the time period to be measured;
taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result;
and comparing each type of data in the optimal clustering result with the characteristics of the electric appliances, and selecting the most similar data as the characteristic value of electric heater load identification.
The power utilization information collected in the time period to be measured comprises: and sampling data of voltage and current in a time period to be monitored by a user.
The active power is calculated according to the following formula;
Figure BDA0001803471830000081
in the formula of UkIs the maximum voltage; i iskIs the maximum value of the current;
Figure BDA0001803471830000082
is the phase difference; p represents active power;
the reactive power is calculated as follows:
Figure BDA0001803471830000083
in the formula, Q represents reactive power.
The fuzzy C-value clustering comprises:
obtaining the membership degree of each sample point to the belonged class center through a clustering objective function, grouping the sample points, and solving the clustering center of each group;
performing iterative computation on the clustering target function to enable the target function to obtain a minimum value;
and when the target function obtains the minimum value, obtaining the optimal clustering result according to a Lagrange multiplier method.
The clustering objective function is shown as follows;
Figure BDA0001803471830000091
in the formula, J is a clustering objective function; u ═ Uij)，uijE (0, 1) is a fuzzy membership matrix, and the sum of the membership degrees of the normalized data set is always equal to 1; c. CiCluster centers for fuzzy group i; m is a weighted index of the membership matrix, used for controlling the fuzzy degree of U, and belongs to [1, ∞ ]; dijAnd the distance between the ith clustering center and the jth data point is shown, wherein the clustering centers comprise no load, small load and large load.
When the objective function obtains the minimum value, obtaining the optimal clustering result according to the Lagrange multiplier method comprises the following steps:
when the target function obtains the minimum value, obtaining the optimal clustering center and the optimal fuzzy membership degree according to a Lagrange multiplier method;
the optimal clustering center is calculated according to the following formula:
Figure BDA0001803471830000092
the optimal fuzzy membership is calculated through an optimal fuzzy membership matrix;
wherein the optimal fuzzy membership matrix is represented by the following formula:
Figure BDA0001803471830000093
the comparing of each kind of data in the optimal clustering result with the electric appliance characteristics and selecting the most similar data as the characteristic value of electric heater load identification comprises the following steps:
calculating the closeness of the actually measured characteristic value and the clustering center, and determining the clustering center;
when the closeness of the actually measured characteristic value and the clustering center meets a first judgment formula, the actually measured characteristic value is closest to the clustering center, and the actually measured characteristic value is subordinate to the clustering center;
and taking the clustering center to which the actually measured characteristic value belongs as the characteristic value for identifying the electric heater load.
The closeness is calculated according to the following formula:
Figure BDA0001803471830000101
in the above formula, N is closeness; mu.sD(U) is the membership of element U to D in the universe of discourse U; mu.sVAnd (U) is the degree of membership of the element U to V in the universe of discourse U.
The first judgment formula is shown as follows:
Figure BDA0001803471830000102
wherein N is closeness; d is calculating the characteristic value of actual measurement; v is the cluster center.
Example 4
A non-intrusive electric heater load identification system, comprising: the device comprises a calculation module, a determination module and a selection module:
the calculation module is used for calculating active power and reactive power according to data collected in advance;
the determining module is used for inputting the active power and the reactive power as fuzzy C value clustering data to obtain a clustering result; iteration is carried out on the clustering result, and the optimal clustering number is determined;
the selection module is used for comparing each type of data of the optimal clustering number with the characteristics of the electric appliances and selecting the most similar data as the characteristic value of electric heater load identification.
The calculation module comprises: a first computation submodule and a second computation submodule;
the active power of the first computing submodule is computed according to the following formula:
Figure BDA0001803471830000103
in the formula of UkIs the maximum voltage; i iskIs the maximum value of the current;
Figure BDA0001803471830000104
is the phase difference; p represents active power;
the reactive power of the second calculation submodule is calculated according to the following formula:
Figure BDA0001803471830000105
in the formula, Q represents reactive power.
The determining module includes: a function submodule and a determination submodule;
the objective function of the function sub-module cluster is shown as follows:
Figure BDA0001803471830000111
in the formula, J is a clustering objective function; u ═ Uij)，uijE (0, 1) is a fuzzy membership matrix, the sum of membership degrees of the normalized data set is always equal to 1, ciFor the cluster center of the fuzzy group i, m ∈ [1, ∞) m is a weighted index of the membership matrix, which is used to control the fuzzy degree of U, dijThe distance between the ith clustering center and the jth data point is defined, wherein the clustering center comprises no load, small load and large load;
the determining submodule is used for enabling the target function to obtain the minimum value through iterative calculation, and obtaining the clustering center and the fuzzy membership matrix of the fuzzy group i according to a Lagrange multiplier method.
The selection module comprises: a calculation submodule and a selection submodule;
the calculation submodule is used for calculating the closeness of the actually measured characteristic value and the clustering center so as to confirm the clustering center;
and the selection submodule is used for selecting the most similar data as the characteristic value of the electric heater according to the similarity between the cluster center and the characteristics of the electric appliance.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (13)

1. A non-intrusive electric heater load identification method is characterized by comprising the following steps:
calculating active power and reactive power according to the power consumption information acquired in the time period to be measured;
taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result;
and comparing each type of data in the optimal clustering result with the characteristics of the electric appliances, and selecting the most similar data as the characteristic value of electric heater load identification.
2. The non-invasive electric heater load identification method of claim 1, wherein the electricity consumption information collected during the time period to be measured comprises: and sampling data of voltage and current in a time period to be monitored by a user.
3. The method of claim 2, wherein the active power is calculated as follows;
Figure FDA0001803471820000011
in the formula of UkIs the maximum voltage; i iskIs the maximum value of the current;
Figure FDA0001803471820000012
is the phase difference; p represents active power; k: is the harmonic order;
the reactive power is calculated as follows:
Figure FDA0001803471820000013
in the formula, Q represents reactive power.
4. The non-intrusive electric heater load identification method of claim 1, wherein the fuzzy C-value clustering comprises:
obtaining the membership degree of each sample point to the belonged class center through a clustering objective function, grouping the sample points, and solving the clustering center of each group;
performing iterative computation on the clustering target function to enable the target function to obtain a minimum value;
and when the target function obtains the minimum value, obtaining the optimal clustering result according to a Lagrange multiplier method.
5. The method of claim 4, wherein the clustering objective function is expressed as follows;
Figure FDA0001803471820000014
in the formula, J is a clustering objective function; u ═ Uij)，uijE (0, 1) is a fuzzy membership matrix, and the sum of the membership degrees of the normalized data set is always equal to 1; c. CiCluster centers for fuzzy group i; m is a weighted index of the membership matrix, used for controlling the fuzzy degree of U, and belongs to [1, ∞ ]; dijAnd the distance between the ith clustering center and the jth data point is shown, wherein the clustering centers comprise no load, small load and large load.
6. The method of claim 4, wherein obtaining the optimal clustering result according to the Lagrangian multiplier method when the objective function is minimized comprises:
when the target function obtains the minimum value, obtaining the optimal clustering center and the optimal fuzzy membership degree according to a Lagrange multiplier method; the optimal fuzzy membership is calculated through an optimal fuzzy membership matrix;
wherein the optimal clustering center is calculated according to the following formula:
Figure FDA0001803471820000021
the optimal fuzzy membership matrix is shown as follows:
Figure FDA0001803471820000022
7. the non-invasive electric heater load identification method of claim 5, wherein the selecting the most similar data as the characteristic value of electric heater load identification based on the comparison of each type of data in the best clustering result with the electric appliance characteristic comprises:
respectively calculating the closeness of the actually measured characteristic value and each cluster center in the optimal clustering result;
when the closeness of the actually measured characteristic value and the clustering center meets a first judgment formula, the actually measured characteristic value is closest to the clustering center, and the actually measured characteristic value is subordinate to the cluster corresponding to the clustering center;
and taking the class to which the actually measured characteristic value belongs as the characteristic value for identifying the load of the electric heater.
8. The non-invasive electric heater load identification method of claim 7,
the closeness is calculated according to the following formula:
Figure FDA0001803471820000031
in the above formula, N is closeness; mu.sD(U) is the membership of element U to D in the universe of discourse U; mu.sVAnd (U) is the degree of membership of the element U to V in the universe of discourse U.
9. The method of claim 7, wherein the first determination formula is as follows:
Figure FDA0001803471820000032
wherein N is closeness; d is calculating the characteristic value of actual measurement; v is the cluster center.
10. A non-intrusive electric heater load identification system, comprising: the device comprises a calculation module, a determination module and a selection module:
the calculation module is used for calculating active power and reactive power according to the power utilization information acquired in the time period to be measured;
the determining module is used for taking the active power and the reactive power as the input of fuzzy C value clustering to obtain an optimal clustering result;
and the selection module is used for comparing each type of data in the optimal clustering result with the characteristics of the electric appliances and selecting the most similar data as the characteristic value of electric heater load identification.
11. The non-intrusive electric heater load identification system of claim 10, wherein the calculation module comprises: a first computation submodule and a second computation submodule;
the active power of the first computing submodule is computed according to the following formula:
Figure FDA0001803471820000033
in the formula of UkIs the maximum voltage; i iskIs the maximum value of the current;
Figure FDA0001803471820000034
is the phase difference; p represents active power; k: is the harmonic order;
the reactive power of the second calculation submodule is calculated according to the following formula:
Figure FDA0001803471820000035
in the formula, Q represents reactive power.
12. The non-intrusive electric heater load identification system of claim 10, wherein the determination module comprises: a function submodule and a determination submodule;
the objective function of the function sub-module cluster is shown as follows:
Figure FDA0001803471820000041
in the formula, J is a clustering objective function; u ═ Uij)，uijE (0, 1) is a fuzzy membership matrix, the sum of membership degrees of the normalized data set is always equal to 1, ciFor the cluster center of the fuzzy group i, m ∈ [1, ∞) m is a weighted index of the membership matrix, which is used to control the fuzzy degree of U, dijThe distance between the ith clustering center and the jth data point is defined, wherein the clustering center comprises no load, small load and large load;
the determining submodule is used for enabling the target function to obtain the minimum value through iterative calculation, and obtaining the clustering center and the fuzzy membership matrix of the fuzzy group i according to a Lagrange multiplier method.
13. The non-intrusive electric heater load identification system of claim 10, wherein the selection module comprises: a calculation submodule and a selection submodule;
the calculation submodule is used for calculating the closeness of the actually measured characteristic value and each clustering center in the optimal clustering result;
the selection submodule is used for enabling the actually measured characteristic value to be closest to the clustering center when the closeness of the actually measured characteristic value and the clustering center meets a first judgment formula, and enabling the actually measured characteristic value to belong to the cluster corresponding to the clustering center; and taking the class to which the actually measured characteristic value belongs as the characteristic value for identifying the load of the electric heater.
CN201811091298.6A 2018-09-18 2018-09-18 Non-invasive electric heater load identification method and system Pending CN110906434A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811091298.6A CN110906434A (en) 2018-09-18 2018-09-18 Non-invasive electric heater load identification method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811091298.6A CN110906434A (en) 2018-09-18 2018-09-18 Non-invasive electric heater load identification method and system

Publications (1)

Publication Number Publication Date
CN110906434A true CN110906434A (en) 2020-03-24

Family

ID=69812969

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811091298.6A Pending CN110906434A (en) 2018-09-18 2018-09-18 Non-invasive electric heater load identification method and system

Country Status (1)

Country Link
CN (1) CN110906434A (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2804012A1 (en) * 2010-08-10 2012-02-16 Sensus Usa Inc. Electric utility meter comprising load identifying data processor
CN103105556A (en) * 2013-01-30 2013-05-15 西安交通大学 Intelligent power grid load testing and recognition method based on steady state and transient state characteristic joint matching
CN103439573A (en) * 2013-08-14 2013-12-11 国家电网公司 Method and system for identifying household loads based on transient characteristic close degree matching
TW201530959A (en) * 2014-01-20 2015-08-01 Univ Nat Taipei Technology Non-invasive load monitoring system and method thereof
CN105823948A (en) * 2016-05-18 2016-08-03 国网上海市电力公司 Non-invasive resident load identification method
CN105972761A (en) * 2016-05-25 2016-09-28 华北电力大学（保定） Non-invasive air conditioner load monitoring method
CN106815677A (en) * 2016-12-09 2017-06-09 国网北京市电力公司 The recognition methods of non-intrusion type load and device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2804012A1 (en) * 2010-08-10 2012-02-16 Sensus Usa Inc. Electric utility meter comprising load identifying data processor
CN103105556A (en) * 2013-01-30 2013-05-15 西安交通大学 Intelligent power grid load testing and recognition method based on steady state and transient state characteristic joint matching
CN103439573A (en) * 2013-08-14 2013-12-11 国家电网公司 Method and system for identifying household loads based on transient characteristic close degree matching
TW201530959A (en) * 2014-01-20 2015-08-01 Univ Nat Taipei Technology Non-invasive load monitoring system and method thereof
CN105823948A (en) * 2016-05-18 2016-08-03 国网上海市电力公司 Non-invasive resident load identification method
CN105972761A (en) * 2016-05-25 2016-09-28 华北电力大学（保定） Non-invasive air conditioner load monitoring method
CN106815677A (en) * 2016-12-09 2017-06-09 国网北京市电力公司 The recognition methods of non-intrusion type load and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
孙毅: "基于差量特征提取与模糊聚类的非侵入式负荷监测方法", 《电力系统自动化》 *
殷桂梁: "基于模糊C均值聚类的非侵入式负荷监测的研究", 《中国高等学校电力系统及其自动化专业第27届学术年会》 *

Similar Documents

Publication Publication Date Title
Liu et al. Energy disaggregation of appliances consumptions using ham approach
CN107229602B (en) Method for identifying electricity consumption behavior of intelligent building microgrid
CN108616120B (en) Non-invasive power load decomposition method based on RBF neural network
CN103135009B (en) Electric appliance detection method and system based on user feedback information
CN110380444B (en) Capacity planning method for distributed wind power orderly access to power grid under multiple scenes based on variable structure Copula
CN111525587B (en) Reactive load situation-based power grid reactive voltage control method and system
CN112884012A (en) Building energy consumption prediction method based on support vector machine principle
CN113837311A (en) Resident customer clustering method and device based on demand response data
CN113159106A (en) Load curve clustering method, medium and equipment based on morphological trend characteristics
Justo et al. Behavioral similarity of residential customers using a neural network based on adaptive resonance theory
CN112990627B (en) Power quality evaluation method
CN116341788A (en) Accurate electric power fingerprint management method for power distribution network line loss analysis
Lu et al. Adaptive weighted fuzzy clustering algorithm for load profiling of smart grid customers
Wen et al. An energy demand-side management and net metering decision framework
Wang et al. Application of clustering technique to electricity customer classification for load forecasting
CN110906434A (en) Non-invasive electric heater load identification method and system
Zhang et al. Time-of-use pricing model considering wind power uncertainty
CN115528684A (en) Ultra-short-term load prediction method and device and electronic equipment
Tito et al. Image segmentation-based event detection for non-intrusive load monitoring using Gramian Angular Summation Field
He et al. Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine
CN111222688A (en) Daily load prediction method for commercial building
Li et al. Non-intrusive load monitoring for consistent shape loads based on convolutional neural network
CN110829409A (en) Non-invasive power load identification method and device
Zhang et al. Load characteristics analysis based on improved k-means clustering algorithm
Shuang et al. A Method for Identifying Connectivity Relationship in Low-Voltage Area Based on Voltage Big Data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200324