CN111614159A - Non-invasive household load identification method for demand side management - Google Patents

Non-invasive household load identification method for demand side management Download PDF

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Publication number
CN111614159A
CN111614159A CN202010397831.2A CN202010397831A CN111614159A CN 111614159 A CN111614159 A CN 111614159A CN 202010397831 A CN202010397831 A CN 202010397831A CN 111614159 A CN111614159 A CN 111614159A
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load
power
identification method
demand side
side management
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Inventor
李飞
王鸿玺
张旭东
高波
李昊洋
孙毅
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
State Grid Hebei Energy Technology Service Co Ltd
Marketing Service Center of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
State Grid Hebei Energy Technology Service Co Ltd
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Priority to CN202010397831.2A priority Critical patent/CN111614159A/en
Publication of CN111614159A publication Critical patent/CN111614159A/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R21/00—Arrangements for measuring electric power or power factor
    • G01R21/001—Measuring real or reactive component; Measuring apparent energy
    • G01R21/002—Measuring real component
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
    • G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/70—Load identification
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances

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  • Engineering & Computer Science (AREA)
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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a non-invasive household load identification method for demand side management, and relates to the technical field of non-invasive load identification; the method comprises the steps of 1-3, wherein in the step 1, mixed load data of a user is collected and processed at a power supply inlet of the user, and processed furniture load data is obtained; step 2, performing switching event detection on the processed furniture load data obtained in the step 1 to obtain a load switching event of the detected electric appliance, wherein the load switching event is an operation switching condition or a change of an operation state; step 3, extracting load characteristics of the load switching event obtained in the step 2 to obtain load characteristics; the household electrical load identification is realized through the steps 1 to 3 and the like.

Description

Non-invasive household load identification method for demand side management
Technical Field
The invention relates to the technical field of non-invasive load identification, in particular to a non-invasive household load identification method for demand side management.
Background
With the development of the smart grid and the proposal of the ubiquitous power internet of things, the smart grid puts higher requirements on interaction of information on both sides of supply and demand and deep mining of data, which requires support of a large amount of user data. The effective demand side management can not only help the power grid side to enhance the operation efficiency of the power grid; and can alleviate energy pressure, improve energy utilization efficiency. With the advance of the management work of the demand side, the load monitoring of the residential user domain is an important factor for realizing the intelligent management of the demand side. The actual energy consumption levels of various loads in the user can be known through load monitoring, and scientific collection and management of energy efficiency data are achieved.
Compared with industrial users and commercial users, the load information of the residential users is distributed discretely, the privacy is strong, and the acquisition is difficult. The non-invasive load monitoring only needs to carry out data acquisition at a user entrance, and the running condition of each load in the user is identified by analyzing information such as total power consumption current and mixed power of the user, so that the information such as the power consumption rule and the energy consumption condition of the user can be known.
Problems with the prior art and considerations:
how to solve the technical problem of household electrical load identification.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a non-invasive household load identification method for demand side management, which realizes household electrical load identification through steps 1-3 and the like.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a non-invasive household load identification method facing demand side management comprises steps 1-3, step 1, collecting and processing mixed load data of a user at a power supply inlet of the user to obtain processed furniture load data;
step 2, performing switching event detection on the processed furniture load data obtained in the step 1 to obtain a load switching event of the detected electric appliance, wherein the load switching event is an operation switching condition or a change of an operation state;
and 3, extracting load characteristics of the load switching event obtained in the step 2 to obtain the load characteristics.
The further technical scheme is as follows: further comprising step 4, step 4: and (4) comparing the load characteristics extracted in the step (3) with the data in the database, and decomposing the power load.
The further technical scheme is as follows: further comprising step 5, step 5: and (4) feeding the power utilization load obtained in the step (4) as a recognition result back to the data center server, and analyzing the power utilization by the data center server according to the power utilization load of the user to obtain the power consumption.
The further technical scheme is as follows: in step 2, X is assumed to be { X ═ XmN is k electrical signals of the current time period.
The further technical scheme is as follows: in the step 2, judging the change condition of the event according to the formula 1;
Figure BDA0002488326070000021
in the formula 1, H0Check for "no change occurred"; h1Checking for "changed"; x is the number ofmIs as followsThe power values of the m electrical signals, in watts; x is the number ofm+1The power value of m +1 electric signals is unit watt; θ is the change threshold in watts.
The further technical scheme is as follows: in the step 3, the active power waveform at the moment when the electric equipment is turned on is used as the load characteristic.
The further technical scheme is as follows: in the step 3, calculating the opening instant energy of each type of load by using the formula 2;
Figure BDA0002488326070000022
in equation 2, v (k-1) is the actual measurement of the voltage at monitoring point k-1 in volts; v (k) is an actual measurement of the voltage at monitoring point k in volts; v (k) is the rate of change of instantaneous voltage at monitoring point k, in volts; i (k-1) is an actual measured value of the current at the monitoring point k-1 and is in ampere; i (k) is the actual measurement of current at monitoring point k in amperes; i (k) is the rate of change of instantaneous current at monitoring point k in amperes; k is the number of the monitoring points, and the number of the total monitoring points is the unit; sTTo turn on the instantaneous energy, in watts.
The further technical scheme is as follows: in the step 4, identifying the electric load by formula 3;
Figure BDA0002488326070000031
in formula 3, AjA characteristic matrix composed of load characteristic quantities of the j-th class of electric loads, a characteristic matrix composed of all characteristics in the whole electric scene, a state matrix composed of the operation states of the M-class of electric loads, and XjIs an element of X, Xj1 is taken to indicate that the type of electrical load is in a working state, xjTaking 0 to represent that the class of electric loads are in a closed state, and d (C, B) represents the distance between the combination of all load characteristics and the total load, and the unit is watt; minimum distance in Watts between the combination of all load characteristics of mind (C, B) and the total load.
The further technical scheme is as follows: in the step 5, the power consumption of each type of load is calculated according to the identification result of each type of power load.
The further technical scheme is as follows: in the step 5, calculating the power consumption of each type of load according to formula 4;
Figure BDA0002488326070000032
in the formula 4, the first step is,
Figure BDA0002488326070000033
is the power consumption of load M during the kth run, in watts; n represents the number of measurements in units; n is the total measurement times and the unit times; t is tnThe time when the household load is started is in unit of minutes; t is tn-1The time of the first minute for the household load to start, in units of minutes; pnThe power monitoring value of the nth minute is unit watt; pn-1The power monitoring value in the previous minute is in watts.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
firstly, a non-invasive household load identification method facing to demand side management comprises the steps of 1-3, wherein in the step 1, mixed load data of a user is collected and processed at a power supply inlet of the user to obtain processed furniture load data; step 2, performing switching event detection on the processed furniture load data obtained in the step 1 to obtain a load switching event of the detected electric appliance, wherein the load switching event is an operation switching condition or a change of an operation state; and 3, extracting load characteristics of the load switching event obtained in the step 2 to obtain the load characteristics. The household electrical load identification is realized through the steps 1 to 3 and the like.
Secondly, the technical scheme can obtain the actual energy consumption level of various loads in the user, and scientific collection and management of energy efficiency data are realized.
Thirdly, the technical scheme can enable a user to timely adjust the power utilization scheme according to the feedback of the data center, so that the power utilization is reasonable, and the effects of energy conservation and emission reduction are achieved.
Fourthly, the technical scheme can enable the user to check the abnormal power utilization equipment as early as possible, and ensure the safe power utilization of the user.
See detailed description of the preferred embodiments.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of an application system of the present invention;
fig. 3 is a power recognition effect diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. 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 application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways than those described herein, and it will be apparent to those of ordinary skill in the art that the present application is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the invention discloses a non-invasive home load identification method facing demand side management, which comprises steps 1 to 5, and specifically comprises the following steps:
step 1, collecting and processing mixed load data of a user at a power supply inlet of the user to obtain processed furniture load data.
And 2, performing switching event detection on the processed furniture load data obtained in the step 1 to obtain a load switching event of the detected electric appliance, wherein the load switching event is an operation switching condition or a change of an operation state.
And 3, extracting load characteristics of the load switching event obtained in the step 2 to obtain the load characteristics.
And 4, step 4: and (4) comparing the load characteristics extracted in the step (3) with the data in the database, and decomposing the power load.
And 5: and (4) feeding the power load obtained in the step (4) as a recognition result back to the data center server, and carrying out energy consumption analysis by the data center according to the power load of the user so as to provide more efficient and convenient energy efficiency service for the user.
In step 2, X is assumed to be { X ═ XmN is k electrical signals of the current time period, and the change condition of the event is judged by formula 1.
Figure BDA0002488326070000051
In the formula 1, H0Check for "no change occurred"; h1Checking for "changed"; x is the number ofmThe power value of the mth electric signal is unit watt; x is the number ofm+1The power value of m +1 electric signals is unit watt; θ is the change threshold in watts.
In the step 3, the active power waveform at the moment of starting the electric equipment is used as the load characteristic, and then the energy at the moment of starting each type of load is calculated by using the formula 2.
Figure BDA0002488326070000052
In equation 2, v (k-1) is the actual measurement of the voltage at monitoring point k-1 in volts; v (k) is an actual measurement of the voltage at monitoring point k in volts; v (k) is the rate of change of instantaneous voltage at monitoring point k, in volts; i (k-1) is an actual measured value of the current at the monitoring point k-1 and is in ampere; i (k) is the actual measurement of current at monitoring point k in amperes; i (k) is the rate of change of instantaneous current at monitoring point k in amperes; k is the number of the monitoring points, and K is the total number of the monitoring points in unit；STTo turn on the instantaneous energy, in watts.
In step 4, the electrical load is identified by equation 3.
Figure BDA0002488326070000061
In formula 3, AjA characteristic matrix composed of load characteristic quantities of the j-th class of electric loads, a characteristic matrix composed of all characteristics in the whole electric scene, a state matrix composed of the operation states of the M-class of electric loads, and XjIs an element of X, Xj1 is taken to indicate that the type of electrical load is in a working state, xjTaking 0 to represent that the class of electric loads are in a closed state, and d (C, B) represents the distance between the combination of all load characteristics and the total load, and the unit is watt; minimum distance in Watts between the combination of all load characteristics of mind (C, B) and the total load.
In the step 5, the power consumption of each type of load is calculated by formula 4 according to the identification result of each type of power load.
Figure BDA0002488326070000062
In the formula 4, the first step is,
Figure BDA0002488326070000063
is the power consumption of load M during the kth run, in watts; n represents the number of measurements in units; n is the total measurement times and the unit times; t is tnThe time when the household load is started is in unit of minutes; t is tn-1The time of the first minute for the household load to start, in units of minutes; pnThe power monitoring value of the nth minute is unit watt; pn-1The power monitoring value in the previous minute is in watts.
The purpose of the application is:
the technical scheme of the application provides a non-invasive household load identification method for supply and demand interaction aiming at the problems of supply and demand interaction of a smart grid, load information acquisition of residents by the smart grid and data mining.
The invention concept of the application is as follows:
most of the existing non-invasive household load identification technologies are based on high-frequency sampling, the requirement on hardware is high, a data acquisition device on a user side such as an intelligent electric meter is low-frequency sampling, and the method is not beneficial to popularization, so that user load data cannot be fully utilized.
Technical contribution of the present application:
in order to solve the problems, the application provides a non-invasive household load identification method for supply and demand interaction. The technical scheme of this application utilizes the sensor of installing at user entrance to carry out data acquisition, through information such as analysis user's power consumption total current and hybrid power come the operational aspect of every load of discernment user inside, and concrete step is as follows:
step 1: collecting and processing mixed load data of a user at a user power supply inlet:
in order to improve the accuracy of the acquired data, the acquired data needs to be denoised and standardized, and then the acquired data is converted into data which is convenient for model identification, namely load characteristic data such as active power, reactive power and the like.
Step 2: switching event detection is carried out on the processed data, and the purpose is to detect the operation switching condition or the change of the operation state of the electric appliance:
let X be { X ═ XmN is k electrical signals of the current time period, and the change condition of the event, H, can be judged by the hypothesis test of formula 10Defined as "no change" test, H1Defined as the test "changed", theta isA set change threshold.
Figure BDA0002488326070000071
And step 3: extracting load characteristics through a load switching event detected by the event:
the technical scheme of the application utilizes the active power waveform of the power utilization equipment at the moment of starting as the load characteristic, and then calculates the energy at the moment of starting various loads by utilizing a formula 2, wherein v (K-1) is the actual measurement value of the voltage at a monitoring point K-1, v (K) is the actual measurement value of the voltage at the monitoring point K, V (K) is the change rate of the instantaneous voltage at the monitoring point K, i (K-1) is the actual measurement value of the current at the monitoring point K-1, i (K) is the actual measurement value of the current at the monitoring point K, I (K) is the change rate of the instantaneous current at the monitoring point K, K is the total number of the monitoring points, and S (K) is the total number of the monitoring pointsTTo turn on the instantaneous energy.
Figure BDA0002488326070000081
And 4, step 4: comparing the extracted load characteristics with data in a database, and decomposing the power load:
the load identification problem of the present application can be described as formula 3, where AjA characteristic matrix composed of load characteristic quantities of the j-th class of electric loads, a characteristic matrix composed of all characteristics in the whole electric scene, a state matrix composed of the operation states of the M-class of electric loads, and Xi1 is taken to indicate that the type of electrical load is in a working state, xiTaking 0 indicates that the class of electrical loads is in the off state, and d (C, B) indicates the distance between the total load and the possible combination of all load characteristics.
Figure BDA0002488326070000082
And 5: the recognition result of the non-invasive household load recognition system is fed back to the data center, and the data center performs energy consumption analysis according to the information, so that more efficient and convenient energy efficiency service is provided for the user:
the power consumption of each type of load can be calculated by formula 4 according to the identification result of each type of power load, wherein,
Figure BDA0002488326070000083
for the power consumption of the load M during the kth operation, tiAnd ti-1Respectively the time of opening the domestic load and the time of one minute before the opening, PiAnd Pi-1Power monitoring values for the nth minute and the previous minute, respectively.
Figure BDA0002488326070000084
The energy consumption analysis of the household load can be carried out through the calculation result, the abnormal electricity utilization behavior of the user can be found, the monitored abnormal condition can be timely fed back to the power user by the data center, the user can adjust the electricity utilization mode and check the abnormal electricity utilization equipment as soon as possible, the electricity utilization mode can be helped to save the electricity charge expense of the user, and the safe electricity utilization of the user can be ensured.
Description of the technical solution:
the embodiment of the application provides a non-invasive household load identification method for supply and demand interaction aiming at the problems of supply and demand interaction of a smart grid, and load information acquisition and data mining of residents by the smart grid.
The non-invasive household load identification method for supply and demand interaction is described in detail below, and specifically includes the following steps 1-5:
step 1, collecting and processing mixed load data of a user at a user power supply inlet:
as shown in fig. 2, for example, a home user is assumed to have a telephone, a television, a refrigerator, an air conditioner, and other power loads, and a monitoring and identifying device is installed at a power inlet of the user, so as to collect and process data through the device.
Step 2, switching event detection is carried out on the processed data, and the purpose is to detect the operation switching condition or the change of the operation state of the electric appliance:
when the state of the electrical load in the home of the user changes, the data collected by the monitoring and identifying device also changes, but the load data has certain fluctuation, so a threshold value theta is set, and when the change value is larger than the threshold value theta, the running state of the electrical load in the home of the user is considered to change.
And 3, extracting load characteristics through the load switching event detected by the event:
as shown in fig. 1, when an event is monitored, load characteristics are extracted, in the present application, an active power waveform at the moment of starting an electrical device is used as the load characteristics, and then, the energy at the moment of starting various loads is calculated by using formula 2.
Step 4, comparing the extracted load characteristics with data in a database, and decomposing the power load:
as shown in fig. 2, the load characteristic library may be obtained through a communication network, and then matched with the extracted load characteristic by equation 3, so as to resolve the power load operation state in the home of the user.
And 5, feeding back the recognition result of the non-invasive household load recognition system to a data center, and carrying out energy consumption analysis by the data center according to the information to provide more efficient and convenient energy efficiency service for users:
as shown in fig. 2, the identification result is obtained and then uploaded to a data center through a communication network, and the data center analyzes the energy consumption and electricity consumption of the user through technologies such as data mining and feeds back the analysis result to the user, so that the user can make adjustments in time, and the electricity is consumed more efficiently and environmentally.
As shown in fig. 3, the recognition effect of the invention is analyzed by taking a refrigerator as an example, and from comparison between the actual power of the refrigerator and the decomposed estimated power, it can be seen that the invention can recognize each state transition process of the refrigerator, the state change of each electrical appliance is regular, when the refrigerator breaks down, the transition power of the refrigerator state changes, and at this time, the data center can find the refrigerator fault in time by analyzing the recognized estimated power change, and inform the user to check in time, so as to ensure the safety of power utilization of the user, and at the same time, can analyze the energy consumption condition of the refrigerator, and similarly, can analyze the electrical appliance condition and the energy consumption condition of the whole family of the user.
After the application runs secretly for a period of time, the feedback of field technicians has the advantages that:
the energy consumption analysis of household loads can be carried out, the abnormal electricity utilization behavior of the user is found, the data center can feed the monitored abnormal condition back to the power user in time, the user can adjust the electricity utilization mode and check the abnormal electricity utilization equipment as soon as possible, the electricity utilization mode can be helped to save electricity charge of the user, and the safe electricity utilization of the user can be ensured.

Claims (10)

1. A non-invasive household load identification method facing demand side management is characterized in that: the method comprises the steps of 1-3, wherein in the step 1, mixed load data of a user is collected and processed at a power supply inlet of the user, and processed furniture load data is obtained;
step 2, performing switching event detection on the processed furniture load data obtained in the step 1 to obtain a load switching event of the detected electric appliance, wherein the load switching event is an operation switching condition or a change of an operation state;
and 3, extracting load characteristics of the load switching event obtained in the step 2 to obtain the load characteristics.
2. The non-invasive household load identification method for demand side management according to claim 1, characterized in that: further comprising step 4, step 4: and (4) comparing the load characteristics extracted in the step (3) with the data in the database, and decomposing the power load.
3. The non-invasive household load identification method for demand side management according to claim 2, characterized in that: further comprising step 5, step 5: and (4) feeding the power utilization load obtained in the step (4) as a recognition result back to the data center server, and analyzing the power utilization by the data center server according to the power utilization load of the user to obtain the power consumption.
4. The non-invasive household load identification method for demand side management according to claim 1, characterized in that: in step 2, X is assumed to be { X ═ XmN is k electrical signals of the current time period.
5. The non-invasive household load identification method for demand side management according to claim 4, characterized in that: in the step 2, judging the change condition of the event according to the formula 1;
Figure FDA0002488326060000011
in the formula 1, H0Check for "no change occurred"; h1Checking for "changed"; x is the number ofmThe power value of the mth electric signal is unit watt; x is the number ofm+1The power value of m +1 electric signals is unit watt; θ is the change threshold in watts.
6. The non-invasive household load identification method for demand side management according to claim 1, characterized in that: in the step 3, the active power waveform at the moment when the electric equipment is turned on is used as the load characteristic.
7. The non-invasive household load identification method for demand side management according to claim 6, characterized in that: in the step 3, calculating the opening instant energy of each type of load by using the formula 2;
Figure FDA0002488326060000021
in equation 2, v (k-1) is the actual measurement of the voltage at monitoring point k-1 in volts; v (k) is an actual measurement of the voltage at monitoring point k in volts(ii) a V (k) is the rate of change of instantaneous voltage at monitoring point k, in volts; i (k-1) is an actual measured value of the current at the monitoring point k-1 and is in ampere; i (k) is the actual measurement of current at monitoring point k in amperes; i (k) is the rate of change of instantaneous current at monitoring point k in amperes; k is the number of the monitoring points, and the number of the total monitoring points is the unit; sTTo turn on the instantaneous energy, in watts.
8. The non-invasive household load identification method for demand side management according to claim 2, characterized in that: in the step 4, identifying the electric load by formula 3;
Figure FDA0002488326060000022
in formula 3, AjA characteristic matrix composed of load characteristic quantities of the j-th class of electric loads, a characteristic matrix composed of all characteristics in the whole electric scene, a state matrix composed of the operation states of the M-class of electric loads, and XjIs an element of X, Xj1 is taken to indicate that the type of electrical load is in a working state, xjTaking 0 to represent that the class of electric loads are in a closed state, and d (C, B) represents the distance between the combination of all load characteristics and the total load, and the unit is watt; minimum distance in Watts between the combination of all load characteristics of mind (C, B) and the total load.
9. The non-invasive household load identification method for demand side management according to claim 3, characterized in that: in the step 5, the power consumption of each type of load is calculated according to the identification result of each type of power load.
10. The non-invasive household load identification method for demand side management according to claim 9, characterized in that: in the step 5, calculating the power consumption of each type of load according to formula 4;
Figure FDA0002488326060000031
in the formula 4, the first step is,
Figure FDA0002488326060000032
is the power consumption of load M during the kth run, in watts; n represents the number of measurements in units; n is the total measurement times and the unit times; t is tnThe time when the household load is started is in unit of minutes; t is tn-1The time of the first minute for the household load to start, in units of minutes; pnThe power monitoring value of the nth minute is unit watt; pn-1The power monitoring value in the previous minute is in watts.
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