CN111259869A - Non-invasive electrical cluster load fault identification method - Google Patents
Non-invasive electrical cluster load fault identification method Download PDFInfo
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Abstract
The invention discloses a non-invasive electrical cluster load fault identification method, which relates to the technical field of electric power, and is characterized in that an electrical load signal on a load bus is obtained, the electrical load signal is denoised, the electrical load signal is identified by adopting an improved K nearest neighbor classification algorithm to obtain a load type corresponding to the electrical load signal, a fault load is obtained according to the load type, the fault of the load is identified according to the fault load, the precision of load fault identification is improved, the early warning of faults and hidden dangers existing in the load is realized, and the problem that an electrical appliance fire monitoring system cannot perform early warning in time is solved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a non-invasive electric cluster load fault identification method.
Background
At present, load faults are mainly identified through a K nearest neighbor classification algorithm, and the algorithm has the following defects:
although the K nearest neighbor classification algorithm considers the class with the maximum probability in the K samples as the class to which the unknown sample belongs, the risk of judging the class depending on the distance of a single sample is reduced to a certain extent, but the algorithm does not provide the basis for determining the K value, so that the blindness of the determination of the K value is caused. Since the K value directly determines the accuracy of the K nearest neighbor classification algorithm, if the K value is too small, the high risk of false identification exists; if the value K is too large, too many samples of incorrect types may be included, and false recognition may be caused.
In summary, the accuracy of load fault identification by using the K-nearest neighbor classification algorithm is not high.
Disclosure of Invention
In order to solve the defects of the prior art, the embodiment of the invention provides a non-intrusive electrical cluster load fault identification method.
The non-intrusive electrical cluster load fault identification method provided by the embodiment of the invention comprises the following steps:
acquiring an electrical load signal on a load bus;
de-noising the electrical load signal;
identifying the electrical load signal by adopting an improved K nearest neighbor classification algorithm to obtain a load type corresponding to the electrical load signal;
obtaining the load with fault according to the load type;
and identifying the fault of the load according to the fault load.
Preferably, identifying the electrical load signal by using a modified K-nearest neighbor based classification algorithm, and obtaining the type of the electrical load signal further comprises:
and pushing the unidentified electrical load signal to a user side for auxiliary processing, and judging whether the load is started or stopped or abnormal electricity utilization conditions exist through the user side.
Preferably, after identifying a fault in the load based on the load in which the fault occurred, the method further comprises:
analyzing the performance characteristics of the load by using a multi-factor coupling analysis method and extracting the performance characteristics by using a wavelet packet and matching tracking method;
and predicting the performance characteristic distribution and the time domain by utilizing a curve fitting technology based on a BP neural network to realize distribution early warning of the performance characteristic.
Preferably, after identifying a fault in the load based on the load in which the fault occurred, the method further comprises:
and analyzing the development trend of the performance characteristics by using an artificial intelligence technology, and realizing very early warning of the performance characteristics.
The non-invasive electrical cluster load fault identification method provided by the embodiment of the invention has the following beneficial effects:
by adopting the improved K nearest neighbor classification algorithm, the accuracy of load fault identification is improved, the early warning of faults and hidden dangers existing in the load is realized, and the problem that an electric appliance fire monitoring system cannot perform early warning in time is solved.
Drawings
Fig. 1 is a schematic flow chart of a non-intrusive electrical cluster load fault identification method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a power time distribution early warning provided in an embodiment of the present invention;
fig. 3 is a schematic diagram of power prediction and early warning provided in the embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
Referring to fig. 1, a non-intrusive electrical cluster load fault identification method provided in an embodiment of the present invention includes the following steps:
s101, acquiring an electrical load signal on a load bus.
And S102, denoising the electrical load signal.
As a specific embodiment, a Kalman filtering method is adopted to denoise the electrical load signal. The Kalman filtering method can estimate the state of a dynamic system from a series of data with measurement noise under the condition that the measurement variance is known. Because the method is convenient for realizing computer programming and can update and process the data acquired on site in real time, the Kalman filtering method is the most widely applied filtering method at present and is better applied to the fields of communication, navigation, guidance, control and the like.
S103, identifying the electrical load signal by adopting an improved K nearest neighbor classification algorithm to obtain a load type corresponding to the electrical load signal.
The improved process based on the improved K nearest neighbor classification algorithm comprises the following steps:
the K nearest neighbor classification algorithm is designed into a three-dimensional characteristic algorithm, a Gaussian function is adopted to carry out weight optimization on samples with different distances, and when the distance between a training sample and a test sample is increased, the weight of the sample with the distance is reduced. Closer neighbors are assigned more weight, while farther neighbors are weighted less accordingly, taking their weighted average. Therefore, the identification precision and the identification efficiency are greatly improved. The value of K is determined by adopting a bp neural network and deep learning training by utilizing a large number of standard data sets, Euclidean distances among n-dimensional space points are calculated by utilizing an Euclidean distance calculation formula, and a weight is added to the distance of each n-dimensional space point by adopting a Gaussian function, so that the points with close distances can obtain larger weights. When the electrical load signal is identified, the weight values predicted for the types of the electrical load signal corresponding to the loads are added, and the load belongs to which type when the weight value of which type of load is the largest.
And S104, obtaining the load with the fault according to the load type.
And S105, identifying the fault of the load according to the fault load.
Optionally, identifying the electrical load signal by using an improved K-nearest neighbor based classification algorithm, and obtaining the type of the electrical load signal further includes:
and pushing the unidentified electrical load signal to a user side for auxiliary processing, and judging whether the load is started or stopped or abnormal electricity utilization conditions exist through the user side.
Optionally, after identifying the load failure according to the load failure, the method further comprises:
analyzing the performance characteristics of the load by using a multi-factor coupling analysis method and extracting the performance characteristics by using a wavelet packet and matching tracking method;
and predicting the performance characteristic distribution and the time domain by utilizing a curve fitting technology based on the BP neural network to realize the distribution early warning of the performance characteristics.
The performance characteristics include temperature, current, power factor, phase angle, power, and frequency when the load is operating.
As a specific example, the time distribution of power is early-warned as shown in fig. 2.
Optionally, after identifying the load failure according to the load failure, the method further comprises:
and the development trend of the performance characteristics is analyzed by utilizing an artificial intelligence technology, and very early warning of the performance characteristics is realized.
As a specific example, the prediction warning of power is shown in fig. 3.
The following tests verify the effect of the non-invasive electrical cluster load fault identification method provided by the embodiment of the invention:
the experimental data come from the special three-phase electric energy metering equipment for high precision, and the verification results are as follows by acquiring the start and stop of the Jiuyang hot water kettle, the incandescent lamp, the energy-saving lamp, the OKEX fan, the American microwave oven, the HP laser printer and the water dispenser for a long time:
according to the non-invasive electrical cluster load fault identification method provided by the embodiment of the invention, the electrical load signals on the load bus are obtained, the electrical load signals are denoised, the electrical load signals are identified by adopting an improved K nearest neighbor classification algorithm to obtain the load types corresponding to the electrical load signals, the fault loads are obtained according to the load types, the faults of the loads are identified according to the fault loads, the precision of load fault identification is improved, the early warning of the faults and hidden dangers of the loads is realized, and the problem that an electrical appliance fire monitoring system cannot give an early warning in time is solved.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It will be appreciated that the relevant features of the method and apparatus described above are referred to one another. In addition, "first", "second", and the like in the above embodiments are for distinguishing the embodiments, and do not represent merits of the embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In addition, the memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
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 above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (4)
1. A non-intrusive electrical cluster load fault identification method is characterized by comprising the following steps:
acquiring an electrical load signal on a load bus;
de-noising the electrical load signal;
identifying the electrical load signal by adopting an improved K nearest neighbor classification algorithm to obtain a load type corresponding to the electrical load signal;
obtaining the load with fault according to the load type;
and identifying the fault of the load according to the fault load.
2. The method of claim 1, wherein identifying the electrical load signal using a modified K-nearest neighbor based classification algorithm, and obtaining the type of electrical load signal further comprises:
and pushing the unidentified electrical load signal to a user side for auxiliary processing, and judging whether the load is started or stopped or abnormal electricity utilization conditions exist through the user side.
3. The method of non-intrusive electrical load identification as defined in claim 1, wherein after identifying a fault with the load based on the load at which the fault occurred, the method further comprises:
analyzing the performance characteristics of the load by using a multi-factor coupling analysis method and extracting the performance characteristics by using a wavelet packet and matching tracking method;
and predicting the performance characteristic distribution and the time domain by utilizing a curve fitting technology based on a BP neural network to realize distribution early warning of the performance characteristic.
4. The method of non-intrusive electrical load identification as defined in claim 1, wherein after identifying a fault with the load based on the load at which the fault occurred, the method further comprises:
and analyzing the development trend of the performance characteristics by using an artificial intelligence technology, and realizing very early warning of the performance characteristics.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686144A (en) * | 2020-12-29 | 2021-04-20 | 中南大学 | Ore ball milling process load identification method based on grinding sound signals |
| CN113325248A (en) * | 2021-04-13 | 2021-08-31 | 清科优能(深圳)技术有限公司 | Intelligent house non-invasive load identification system based on edge calculation |
| CN119134336A (en) * | 2024-11-12 | 2024-12-13 | 中科技术物理苏州研究院 | Photovoltaic power prediction method and system based on clustering and hybrid neural network |
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| CN108459222A (en) * | 2018-03-22 | 2018-08-28 | 中国海洋大学 | A kind of electric appliance fault detection method and system |
| CN109165604A (en) * | 2018-08-28 | 2019-01-08 | 四川大学 | The recognition methods of non-intrusion type load and its test macro based on coorinated training |
| CN109376752A (en) * | 2018-08-28 | 2019-02-22 | 北京邮电大学 | A PTM-WKNN classification method and device based on imbalanced data set |
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2020
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| WO2002037653A2 (en) * | 2000-10-30 | 2002-05-10 | Ng, Yum-Meng | A method and apparatus for automatically detecting and managing an ac power fault |
| CN108459222A (en) * | 2018-03-22 | 2018-08-28 | 中国海洋大学 | A kind of electric appliance fault detection method and system |
| CN109165604A (en) * | 2018-08-28 | 2019-01-08 | 四川大学 | The recognition methods of non-intrusion type load and its test macro based on coorinated training |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112686144A (en) * | 2020-12-29 | 2021-04-20 | 中南大学 | Ore ball milling process load identification method based on grinding sound signals |
| CN112686144B (en) * | 2020-12-29 | 2022-03-08 | 中南大学 | Ore ball milling process load identification method based on grinding sound signals |
| CN113325248A (en) * | 2021-04-13 | 2021-08-31 | 清科优能(深圳)技术有限公司 | Intelligent house non-invasive load identification system based on edge calculation |
| CN119134336A (en) * | 2024-11-12 | 2024-12-13 | 中科技术物理苏州研究院 | Photovoltaic power prediction method and system based on clustering and hybrid neural network |
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Application publication date: 20200609 |