CN112560889A - Power load identification method - Google Patents
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- CN112560889A CN112560889A CN202011226513.6A CN202011226513A CN112560889A CN 112560889 A CN112560889 A CN 112560889A CN 202011226513 A CN202011226513 A CN 202011226513A CN 112560889 A CN112560889 A CN 112560889A
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- 238000001514 detection method Methods 0.000 claims description 21
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 230000001052 transient effect Effects 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000003708 edge detection Methods 0.000 claims description 3
- 230000006399 behavior Effects 0.000 abstract description 7
- 230000005611 electricity Effects 0.000 abstract description 3
- 238000000605 extraction Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 238000009434 installation Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012567 pattern recognition method Methods 0.000 description 3
- 238000007418 data mining Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003657 Likelihood-ratio test Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
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- 238000005070 sampling Methods 0.000 description 1
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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Abstract
The application provides a power load identification method, which comprises the steps of obtaining characteristics representing a load according to the power utilization characteristics of the load; comparing the characteristics with a characteristic library of the electric equipment, and identifying the electric equipment corresponding to the load; and combining the characteristics with the electrical characteristics of the corresponding electric equipment to improve the recognition degree of the characteristics. The method provides the characteristics representing the load behaviors of residents through the electricity utilization characteristics of the resident loads; introducing a statistical description method for the load characteristic parameters aiming at the uncertainty characteristics of the load behaviors and the electrical parameters; a behavioral-characteristic-based NILM method applicable to residential smart meters is presented.
Description
Technical Field
The application relates to the technical field of power load identification, in particular to a power load identification method.
Background
Under the environment of an intelligent power grid, intelligent measuring equipment is gradually and widely applied, so that accurate and massive user load data can be obtained; the data mining method is utilized to process the big data of the user load, and useful information can be extracted from the big data, so that the power load can be understood more systematically and deeply, and the load management level and the safety of system operation are improved; load identification is an important area where big data mining is applied in power systems.
At present, the resident electrical load identification equipment mostly adopts an intrusive monitoring method, the intrusive monitoring method needs to install an intermediate monitoring device between the electrical equipment and a socket to record the condition of the equipment, and the intrusive monitoring method usually depends on the intermediate device to monitor the operation record of the load equipment, the energy consumption data of the equipment and the like.
However, the intrusive load identification method requires installation of hardware equipment between the electric equipment and the socket, and as the number of users to be monitored increases with the increase of the electric equipment, a large number of hardware equipment is required, the hardware equipment itself consumes electric power, and in addition, the installation requires entry into a user room, resulting in inconvenience in installation and maintenance.
Disclosure of Invention
The application provides a power load identification method, which aims to solve the technical problem that an intrusive load identification method requires a large amount of hardware equipment along with the increase of monitored users and electric equipment, and the intrusive load identification method is inconvenient to install and maintain as the intrusive load identification method needs to enter a user room during installation.
In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:
there is provided a power load identification method, the method comprising:
acquiring characteristics representing the load according to the power utilization characteristics of the load;
comparing the characteristics with a characteristic library of the electric equipment, and identifying the electric equipment corresponding to the load;
and combining the characteristics with the electrical characteristics of the corresponding electric equipment to improve the recognition degree of the characteristics.
Optionally, the power consumption characteristics of the load include:
expressing the characteristics of different loads through probability functions;
the degree of importance of the different characteristics in determining the load classification is expressed by a membership function.
Optionally, the features include steady-state features and transient-state features;
the steady state characteristics comprise voltage, current, fundamental wave active power and fundamental wave reactive power.
Optionally, before the obtaining, according to the power consumption characteristics of the load, the basic characteristic quantity characterizing the load behavior, the method further includes:
acquiring a steady-state signal and a transient-state signal of the load through data measurement;
performing data processing on the steady-state signal and the transient-state signal; the data processing comprises denoising, electric quantity calculation and per unit;
and detecting an event according to the change of the processed steady-state signal and the transient-state signal.
Optionally, the event detection comprises:
the accuracy of event detection and matching is improved through an event detection algorithm of power increment and an event matching algorithm based on the power increment and current waveform.
Optionally, the event detection further comprises:
and when the change exceeds a preset threshold value, judging that time is available.
Optionally, the event detection may also be obtained by an edge detection algorithm.
The application provides a power load identification method, which comprises the steps of obtaining characteristics representing a load according to the power utilization characteristics of the load; comparing the characteristics with a characteristic library of the electric equipment, and identifying the electric equipment corresponding to the load; and combining the characteristics with the electrical characteristics of the corresponding electric equipment to improve the recognition degree of the characteristics. The method provides the characteristics representing the load behaviors of residents through the electricity utilization characteristics of the resident loads; introducing a statistical description method for the load characteristic parameters aiming at the uncertainty characteristics of the load behaviors and the electrical parameters; a behavioral-characteristic-based NILM method applicable to residential smart meters is presented.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a power load identification method according to an embodiment of the present application;
FIG. 2 is a block diagram of a non-intrusive load detection system of the present application;
FIG. 3 is a graph showing a voltage characteristic parameter of an electric device according to an embodiment of the present disclosure;
FIG. 4 is a diagram of a current characteristic parameter of an electric device according to an embodiment of the present disclosure;
FIG. 5 is a distribution diagram of an active power characteristic parameter of a fundamental wave of an electrical device according to an embodiment of the present application;
FIG. 6 is a distribution diagram of fundamental wave reactive power characteristic parameters of an electrical device according to an embodiment of the present application;
fig. 7 is a schematic diagram of a three-hour load change in a power load identification method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all embodiments. 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.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Non-Intrusive Load Monitoring (NILM) technology can decompose time-sharing Energy consumption and running state information of different types of loads in a power supply range through detection and analysis of voltage and current data of a centralized power supply point, and provide effective Energy Management basic information for a household Energy Management System (REMS). The prior NILM technology has the problems of high requirement on a sampling device and large solving calculation amount, and is difficult to be directly and widely used in large-scale residential users.
The present application is described in further detail below with reference to the attached drawing figures:
the embodiment of the application provides a power load identification method, which is used in the field of power load identification and comprises the following steps:
and acquiring a steady-state signal and a transient-state signal of the load through data measurement. The steady-state signal and the transient-state signal have measurement errors: firstly, the inconsistency of the measuring devices is that different measuring devices have different measured values for the same electric equipment; secondly, the sensor causes data loss due to the processes of compressing and transmitting the original data.
Performing data processing on the steady-state signal and the transient-state signal; the data processing comprises denoising, electric quantity calculation and per unit. The primary purpose of per unit is to facilitate handling of interference problems caused by power quality fluctuations.
As shown in fig. 2, data measurement and processing are the preparation of NILM, and event detection, feature extraction, and load identification are key technologies.
And detecting an event according to the change of the processed steady-state signal and the transient-state signal. The accuracy of event detection and matching is improved through an event detection algorithm of power increment and an event matching algorithm based on the power increment and current waveform. And when the change exceeds a preset threshold value, judging that time is available. The event detection may also be obtained by an edge detection algorithm. Upon detection of an event, feature extraction may be performed, i.e., a series of different load signature features for use by the load are extracted from the data before and after the event. In order to be able to identify loads similar to the load signature, the extracted features should be reasonably efficient. Of course, different features will be obtained depending on the selected load signature and the extraction method.
Event detection can also be attributed to the problem of variable point detection. A change point refers to a time when a statistical property of a portion of a sequence or process changes. After the time is found, whether a new event occurs is detected by carrying out likelihood ratio test or signal mutation detection algorithm before and after the time. As shown in fig. 7, the point of the severe load change, that is, the moment when a new event occurs, is detected by the change point detection, and the start and stop of the device and the change of the state are perceived.
Referring to fig. 1, the method further includes the steps of:
s100, acquiring characteristics representing the load according to the power utilization characteristics of the load; the power utilization characteristics of the load comprise: expressing the characteristics of different loads through probability functions; the degree of importance of the different characteristics in determining the load classification is expressed by a membership function.
And after detecting that the electric equipment is put into use, further extracting the characteristics of the load imprint. The features are divided into steady-state features and transient-state features, and accordingly, the extraction technology can be classified into three methods based on the steady-state features, the transient-state features and comprehensive consideration of the steady-state features and the transient-state features. The feature extraction technology based on the steady-state features cannot effectively cope with some scenes with high identification difficulty, such as similarity and superposition of features of electric equipment; the transient feature-based extraction technique is more adaptive because the load signatures of the transient features are more reflective of the characteristics and functions of different devices. In addition, transient events are short and there is less likelihood of feature overlap. But transient feature extraction puts higher requirements on data acquisition and processing. The steady state characteristics comprise voltage, current, fundamental wave active power and fundamental wave reactive power.
As shown in fig. 3-6. The lines are respectively the line graphs of four characteristics of voltage, current, fundamental wave active power and fundamental wave reactive power of different household appliances, and different electrical equipment can be identified through the line graphs of the current characteristics. Of course, different electrical appliances and the same electrical appliance have different characteristic parameters in different operation modes and states. Taking an air conditioner as an example, the active power of the air conditioner is greatly different from the reactive power of the air conditioner during cooling and heating, and the reactive power of the air conditioner is also obviously different. The differences of the characteristic parameters of the electric appliances can be obviously seen by the line graphs, and the differences can be separated by utilizing the differences as shown in figure 3.
S200, comparing the characteristics with a characteristic library of the electric equipment, and identifying the electric equipment corresponding to the load; the establishment of a feature library of electric equipment is very critical, and at present, two main methods are available: firstly, recording the load imprint characteristics of the electric equipment under the assistance of manpower; secondly, automatic classification is carried out through an algorithm.
And S300, combining the characteristics with the electrical characteristics of the corresponding electric equipment to improve the identification degree of the characteristics.
And (3) given a feature library of the electric equipment and load features extracted from the collected data, identifying the components of the total load and realizing load decomposition. This seemingly simple problem is mathematically difficult to model and solve. Because the model is difficult to solve;
the method is characterized in that a complete electric equipment feature library is required to be accurately identified, and the modeling is carried out on the premise that features can be superposed or subjected to mathematical operation. Therefore, the method can be realized by adopting a pattern recognition method, and the load recognition based on the pattern recognition is realized by learning the load imprint characteristics of various electric equipment. The pattern recognition method includes both supervised learning and unsupervised learning. The pattern recognition method usually learns and trains load characteristics, the process is complicated, and the required samples are large, so how to simplify the training process, reduce the calculated amount and improve the recognition accuracy rate is the key point of research.
Non-intrusive load monitoring measures the voltage, current, etc. of the total load as signals carrying power information, which contains information on different characteristic load components. By extracting the characteristic information of these electrical quantities, the NILM system can achieve load shedding. That is, by detecting electric power signals such as voltage and current of the total load, it is possible to detect what electric appliance the user is using. The electric equipment characteristic information reflects unique information reflecting the electric utilization state, such as voltage, active waveform, starting current and the like, embodied by electric equipment in operation. The characteristics are determined by the working conditions of the electric equipment, and the load marks can be classified into a steady state, a transient state and an operation mode according to the characteristics, wherein the steady state and the transient state depend on the characteristics of components inside the equipment; the operating mode is determined by the operating control strategy of the device. During operation of the device, these load signatures are repeated, on the basis of which the individual appliances can be identified.
The application provides a power load identification method, which comprises the steps of obtaining characteristics representing a load according to the power utilization characteristics of the load; comparing the characteristics with a characteristic library of the electric equipment, and identifying the electric equipment corresponding to the load; and combining the characteristics with the electrical characteristics of the corresponding electric equipment to improve the recognition degree of the characteristics. The method provides the characteristics representing the load behaviors of residents through the electricity utilization characteristics of the resident loads; introducing a statistical description method for the load characteristic parameters aiming at the uncertainty characteristics of the load behaviors and the electrical parameters; a behavioral-characteristic-based NILM method applicable to residential smart meters is presented.
The above-mentioned contents are only for explaining the technical idea of the present application, and the protection scope of the present application is not limited thereby, and any modification made on the basis of the technical idea presented in the present application falls within the protection scope of the claims of the present application.
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Claims (7)
1. A power load identification method, the method comprising:
acquiring characteristics representing the load according to the power utilization characteristics of the load;
comparing the characteristics with a characteristic library of the electric equipment, and identifying the electric equipment corresponding to the load;
and combining the characteristics with the electrical characteristics of the corresponding electric equipment to improve the recognition degree of the characteristics.
2. The method according to claim 1, wherein the power consumption characteristics of the load comprise:
expressing the characteristics of different loads through probability functions;
the degree of importance of the different characteristics in determining the load classification is expressed by a membership function.
3. A power load identification method according to claim 1 or 2, wherein said characteristics comprise steady state characteristics and transient state characteristics;
the steady state characteristics comprise voltage, current, fundamental wave active power and fundamental wave reactive power.
4. The method according to claim 1, wherein before the obtaining of the basic characteristic quantity characterizing the load behavior according to the power consumption characteristics of the load, the method further comprises:
acquiring a steady-state signal and a transient-state signal of the load through data measurement;
performing data processing on the steady-state signal and the transient-state signal; the data processing comprises denoising, electric quantity calculation and per unit;
and detecting an event according to the change of the processed steady-state signal and the transient-state signal.
5. The power load identification method according to claim 4, wherein the event detection comprises:
the accuracy of event detection and matching is improved through an event detection algorithm of power increment and an event matching algorithm based on the power increment and current waveform.
6. The power load identification method of claim 4, wherein the event detection further comprises:
and when the change exceeds a preset threshold value, judging that time is available.
7. A power load identification method according to claim 4, characterized in that said event detection is also obtained by means of an edge detection algorithm.
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