CN113920375A - Fusion characteristic typical load recognition method and system based on combination of Faster R-CNN and SVM - Google Patents

Fusion characteristic typical load recognition method and system based on combination of Faster R-CNN and SVM Download PDF

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CN113920375A
CN113920375A CN202111284397.8A CN202111284397A CN113920375A CN 113920375 A CN113920375 A CN 113920375A CN 202111284397 A CN202111284397 A CN 202111284397A CN 113920375 A CN113920375 A CN 113920375A
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李宁
齐尚敏
刘海洋
余英
曾婧
杨永建
胡慧敏
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Marketing Service Center Of State Grid Xinjiang Electric Power Co Ltd Capital Intensive Center Metering Center
Dalian University of Technology
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Abstract

The invention relates to a fusion characteristic typical load recognition method and a system based on combination of fast R-CNN and SVM, wherein the method comprises the following steps: s1: collecting the load of electrical equipment, constructing a signal data sample, selecting a corresponding wavelet base, and determining the number of decomposition layers; decomposing the noisy signals to obtain a group of wavelet coefficients; s2: performing soft threshold function processing on the wavelet coefficient to obtain an estimated wavelet coefficient; s3: reconstructing the wavelet by using the estimated wavelet coefficient to obtain a de-noised reconstruction signal; s4: carrying out normalization processing on the reconstruction signal to obtain a normalized reconstruction signal; s5: and constructing a load characteristic curve image based on the normalized reconstructed signal, performing characteristic extraction on the load characteristic curve image by using an Faster R-CNN network, and classifying by using an SVM (support vector machine) to obtain a final load identification result. The method provided by the invention improves the accuracy of typical load characteristic identification, guides energy conservation, reduces the power consumption cost of users and improves the utilization rate of electric energy.

Description

Fusion characteristic typical load recognition method and system based on combination of Faster R-CNN and SVM
Technical Field
The invention relates to the field of intelligent electric meters, in particular to a fusion characteristic typical load identification method and system based on combination of Faster R-CNN and SVM.
Background
The charge control intelligent electric meter is directly installed on a user total current inlet bus to collect power information parameters of a plurality of electric equipment in a user house as an intelligent metering instrument. The system can carry out multiple accurate measurement records on the electric energy and support real-time multi-rate of electricity price, and is widely applied. Load identification is an important technology of the fee-controlled intelligent electric meter, and the total power can be refined into the load. The power utilization list of the main electric appliances is provided for the user, and guidance is given to energy conservation, so that the power utilization cost of the user can be reduced, and the utilization rate of electric energy can be improved.
Load identification is broadly divided into two categories, invasive and non-invasive. The non-invasive load identification is to identify the electrical characteristics of the load such as current, power and the like when the current is in the house. Non-invasive load identification refers to the installation of identification devices at each type of electrical consumer interface. Since the installation, maintenance and other operations of the intrusive load identification device are difficult to realize, the non-intrusive load identification is widely accepted. The non-invasive load identification technology of the intelligent electric meter is an important electric energy classification metering technology in an intelligent power grid and is also one of important measures for realizing energy conservation and emission reduction. At present, the defects of precision loss and confusion of similar load characteristic electric appliances still exist when the load is identified by utilizing the identification capability of a convolutional neural network to an image, so that how to improve the identification accuracy needs to be solved urgently.
Disclosure of Invention
In order to solve the technical problems, the invention provides a fusion characteristic typical load identification method and system based on the combination of Faster R-CNN and SVM.
The technical solution of the invention is as follows: a fusion characteristic typical load recognition method based on combination of fast R-CNN and SVM comprises the following steps:
step S1: selecting n days of a day to be measured, collecting M point loads of the electrical equipment each day, constructing a signal data sample to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics, and determining the number of decomposition layers; decomposing the noisy signals to obtain a group of wavelet coefficients;
step S2: determining a threshold value of each wavelet coefficient of each layer in a grading manner, and performing soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients;
step S3: reconstructing the wavelet by using the estimated wavelet coefficient to obtain a de-noised reconstructed signal;
step S4: carrying out normalization processing on the reconstruction signal to obtain a normalized reconstruction signal;
step S5: constructing a load characteristic curve image based on the normalized reconstruction signal, performing characteristic extraction on the load characteristic curve image by using an Faster R-CNN network to obtain a characteristic diagram, calculating to obtain an interested region, generating an interested region characteristic diagram, generating a characteristic sequence based on the interested region characteristic diagram, obtaining a characteristic sequence with weight by using an attention mechanism, and classifying the characteristic sequence with weight by using an SVM to obtain a final load identification result; wherein the Faster R-CNN network comprises: the method comprises the steps of a feature extraction network, a regional suggestion network, a regional feature map generation network and a classification and regression detection network.
Compared with the prior art, the invention has the following advantages:
the invention provides a fusion characteristic typical load recognition method based on Fast R-CNN, a Support Vector Machine (SVM) and an attention mechanism, wherein the Fast R-CNN has super-strong learning ability and can realize characteristic extraction by using a small amount of sample data, and the network is more suitable for capturing local characteristics; the introduced attention mechanism can focus on the relationship between elements in the global sequence more clearly, and the two methods are combined to carry out more efficient learning and identification on local features and global relations in the feature sequence. In addition, the invention uses a support vector machine for classification, and combines the generalization capability of the SVM to the small sample data set with the feature extraction capability of the Faster R-CNN, thereby improving the applicability of the whole model to the small sample data set, improving the accuracy of typical load feature identification, guiding energy conservation, reducing the electricity consumption cost of users and improving the utilization rate of electric energy.
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FIG. 1 is a flowchart of a typical load recognition method based on fusion characteristics of combination of Faster R-CNN and SVM in the embodiment of the present invention;
FIG. 2 is a schematic diagram of an attention mechanism in a Faster R-CNN network according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fast R-CNN network according to an embodiment of the present invention;
FIG. 4 is a block diagram of a typical load recognition system based on fusion characteristics of the combination of Faster R-CNN and SVM in the embodiment of the present invention.
Detailed Description
The invention provides a fusion characteristic typical load identification method based on combination of fast R-CNN and SVM, which improves the accuracy of typical load characteristic identification, guides energy conservation, reduces the electricity consumption cost of users and improves the utilization rate of electric energy.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings.
Example one
As shown in fig. 1, a typical load recognition method based on fusion characteristics of combination of fast R-CNN and SVM according to an embodiment of the present invention includes the following steps:
step S1: selecting n days of a day to be measured, collecting M point loads of the electrical equipment each day, constructing a signal data sample to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics, and determining the number of decomposition layers; decomposing the noisy signals to obtain a group of wavelet coefficients;
step S2: determining a threshold value of each wavelet coefficient of each layer in a grading manner, and performing soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients;
step S3: reconstructing the wavelet by using the estimated wavelet coefficient to obtain a de-noised reconstruction signal;
step S4: carrying out normalization processing on the reconstruction signal to obtain a normalized reconstruction signal;
step S5: based on the normalized reconstruction signal, constructing a load characteristic curve image, performing characteristic extraction on the load characteristic curve image by using an Faster R-CNN network to obtain a characteristic diagram, calculating to obtain an interested region, generating a characteristic diagram of the interested region, generating a characteristic sequence based on the characteristic diagram of the interested region, obtaining a characteristic sequence with weight by using an attention mechanism, and classifying the characteristic sequence with weight by using an SVM to obtain a final load identification result; wherein, the Faster R-CNN network comprises: the method comprises the steps of a feature extraction network, a regional suggestion network, a regional feature map generation network and a classification and regression detection network.
In one embodiment, the step S1: selecting n days of a day to be measured, constructing a signal data sample by using M load points each day to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics, and determining the number j of decomposition layers; decomposing the noisy signal to obtain a group of wavelet coefficients, specifically comprising:
step S11: selecting n days of a day to be measured, collecting M point loads of the electrical equipment each day, constructing a signal data sample, and obtaining a load characteristic set as X ═ X1,x2,...,xi,...,xNN is the number of load characteristics of the electrical equipment, xiIs the ith characteristic in the load characteristic set X;
step S12: a noisy signal is calculated according to the following equation (1):
g(k)＝f(k)+s(k)k＝1,2,3,…,M-1 (1)
wherein k is time, f (k) is real signal, s (k) is noise signal, g (k) is noise-containing signal;
step S13: selecting a corresponding wavelet basis based on the noise-containing signals g (k), and selecting a smooth continuous wavelet basis when the noise-containing signals are smooth; when the linear property of the noisy signal is stronger, selecting a linear wavelet base;
step S14: decomposing the noise-containing signals g (k) according to a preset decomposition layer number j to obtain a group of wavelet coefficients omegaj,k。
In one embodiment, the step S2: determining a threshold value of each wavelet coefficient of each layer in stages, and performing soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients, wherein the method specifically comprises the following steps:
performing soft threshold function processing on the wavelet coefficient according to formula (2) to obtain an estimated wavelet coefficient
Figure BDA0003332310650000041
Figure BDA0003332310650000042
Wherein T is a threshold value; when wavelet coefficient omegaj,kWhen the absolute value of (1) is greater than T, the wavelet coefficient is estimated
Figure BDA0003332310650000043
Has an absolute value of | ωj,kl-T, the sign remains unchanged; when wavelet coefficient omegaj,kIs less than or equal to T, the wavelet coefficient is estimated
Figure BDA0003332310650000044
Is zero.
In one embodiment, the step S3: using estimated wavelet coefficients
Figure BDA0003332310650000045
Reconstructing the wavelet to obtain a de-noised reconstructed signal
Figure BDA0003332310650000046
In one embodiment, the step S4: for the reconstructed signal
Figure BDA0003332310650000047
And carrying out normalization processing to normalize the value to a (0, 1) interval to obtain a normalized reconstruction signal.
The preprocessing operation is carried out on the data samples through the steps S1-S4, and the noise processing and normalization processing are carried out on the images through wavelet denoising, so that the influence of bad data on the subsequent neural network training is weakened.
In one embodiment, the step S5: based on the normalized reconstruction signal, constructing a load characteristic curve image, performing characteristic extraction on the load characteristic curve image by using an Faster R-CNN network to obtain a characteristic diagram, calculating to obtain an interested region, generating a characteristic diagram of the interested region, generating a characteristic sequence based on the characteristic diagram of the interested region, obtaining a characteristic sequence with weight by using an attention mechanism, and classifying the characteristic sequence with weight by using an SVM to obtain a final load identification result; wherein, the Faster R-CNN network comprises: the method comprises the following steps of extracting a network, suggesting a network in a region, generating a network by a regional characteristic graph and classifying and regressing a detection network, and specifically comprises the following steps:
step S51: constructing a load characteristic curve image based on the normalized reconstruction signal: inputting the load characteristic curve image into a characteristic extraction network to obtain a characteristic diagram;
the embodiment of the invention adopts a VGG network to realize the feature extraction of the load feature curve image;
step S52: inputting the feature map into a region suggestion network (RPN), and calculating to obtain a target region to be identified on the feature map to obtain a region of interest;
step S53: inputting the characteristic diagram of the region of interest into a network, generating the characteristic diagram of the region of interest by using ROI poolingCalculating the over-convolution layer to obtain the characteristic sequence
Figure BDA0003332310650000048
Computing feature sequences using an attention mechanism
Figure BDA0003332310650000049
Obtaining a weighted characteristic sequence y ═ y1,y2,...,yt,...,yN]The method comprises the following specific steps:
according to the following formula (3), a query vector q ═ q is calculated1,q2,...,qi,...,qN]The key vector m ═ m1,m2,...,mi,...,mN]Sum vector v ═ v1,v2,...,vi,...,vN]：
Figure BDA0003332310650000051
Wherein, Wq、WmAnd WvIs a preset weight matrix coefficient;
according to equation (4), each is calculated
Figure BDA0003332310650000052
Attention score of ct,iI.e. at present
Figure BDA0003332310650000053
Correlation between the power value at time t and the power values of other vectors in the signature sequence;
ct,i＝qt·mi (4)
the attention score is normalized according to equation (5):
Figure BDA0003332310650000054
the normalized attention scores are weighted and summed according to equation (6):
Figure BDA0003332310650000055
wherein, ytWeighted sequence y ═ y for output after attention mechanism calculation1,y2,...,yt,...,yN]The value at time t.
FIG. 2 is a schematic diagram of the attention mechanism in the Faster R-CNN network.
In the training process of the Faster R-CNN network, the embodiment of the invention allocates a weight coefficient to each element in the characteristic sequence by introducing an attention mechanism, so that the attention of the network can be put on the details such as the peak-valley length with more judgment information, and the like, and the influence of other fluctuations on the classification result is weakened.
Step S54: inputting the sequences with weights into a classification and regression detection network, and classifying the sequences by using a one-to-one method Support Vector Machine (SVM), wherein the classification specifically comprises the following steps:
according to the weighted sequence y ═ y1,y2,...,yt,...,yN]And classifying the classes by using an SVM to obtain z classes, judging again by using z (z-1)/2 secondary classifiers, voting for the corresponding classes, and taking the class with the most votes as a final load identification result.
According to the embodiment of the invention, the SVM is selected as the feature classifier, so that the generalization capability of small samples can be improved.
FIG. 3 is a schematic diagram of the fast R-CNN network structure.
The invention provides a fusion characteristic typical load recognition method based on Fast R-CNN, a Support Vector Machine (SVM) and an attention mechanism, wherein the Fast R-CNN has super-strong learning ability and can realize characteristic extraction by using a small amount of sample data, and the network is more suitable for capturing local characteristics; the introduced attention mechanism can focus on the relationship between elements in the global sequence more clearly, and the two methods are combined to carry out more efficient learning and identification on local features and global relations in the feature sequence. In addition, the invention uses a support vector machine for classification, and combines the generalization capability of the SVM to the small sample data set with the feature extraction capability of the Faster R-CNN, thereby improving the applicability of the whole model to the small sample data set, improving the accuracy of typical load feature identification, guiding energy conservation, reducing the electricity consumption cost of users and improving the utilization rate of electric energy.
Example two
As shown in fig. 4, an embodiment of the present invention provides a fusion characteristic typical load recognition system based on the combination of fast R-CNN and SVM, which includes the following modules:
the wavelet coefficient acquisition module 61 is used for selecting n days to be measured, acquiring M point loads of the electrical equipment each day, constructing a signal data sample to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics, and determining the decomposition layer number; decomposing the noisy signals to obtain a group of wavelet coefficients;
an estimated wavelet coefficient obtaining module 62, configured to determine a threshold of each layer of wavelet coefficients in a staged manner, and perform soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients;
a reconstructed signal module 63, configured to reconstruct the wavelet by using the estimated wavelet coefficient to obtain a denoised reconstructed signal;
a normalization module 64, configured to perform normalization processing on the reconstructed signal to obtain a normalized reconstructed signal;
the load identification module 65 is configured to construct a load characteristic curve image based on the normalized reconstructed signal, perform characteristic extraction on the load characteristic curve image by using an fast R-CNN network to obtain a characteristic diagram, calculate an interested region, generate an interested region characteristic diagram, generate a characteristic sequence based on the interested region characteristic diagram, obtain a characteristic sequence with weights by using an attention mechanism, and classify the characteristic sequence to obtain a final load identification result; wherein, the Faster R-CNN network comprises: the method comprises the steps of a feature extraction network, a regional suggestion network, a regional feature map generation network and a classification and regression detection network.
The above examples are provided only for the purpose of describing the present invention, and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalent substitutions and modifications can be made without departing from the spirit and principles of the invention, and are intended to be within the scope of the invention.

Claims (5)

1. A fusion characteristic typical load recognition method based on combination of fast R-CNN and SVM is characterized by comprising the following steps:
step S1: selecting n days of a day to be measured, collecting M point loads of the electrical equipment each day, constructing a signal data sample to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics, and determining the number of decomposition layers; decomposing the noisy signals to obtain a group of wavelet coefficients;
step S2: determining a threshold value of each wavelet coefficient of each layer in a grading manner, and performing soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients;
step S3: reconstructing the wavelet by using the estimated wavelet coefficient to obtain a de-noised reconstructed signal;
step S4: carrying out normalization processing on the reconstruction signal to obtain a normalized reconstruction signal;
step S5: constructing a load characteristic curve image based on the normalized reconstruction signal, performing characteristic extraction on the load characteristic curve image by using an Faster R-CNN network to obtain a characteristic diagram, calculating to obtain an interested region, generating an interested region characteristic diagram, generating a characteristic sequence based on the interested region characteristic diagram, obtaining a characteristic sequence with weight by using an attention mechanism, and classifying the characteristic sequence with weight by using an SVM to obtain a final load identification result; wherein the Faster R-CNN network comprises: the method comprises the steps of a feature extraction network, a regional suggestion network, a regional feature map generation network and a classification and regression detection network.
2. The fusion feature canonical load recognition method based on the combination of fast R-CNN and SVM according to claim 1, wherein the step S1: selecting n days of a day to be measured, constructing a signal data sample by using M load points each day to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics, and determining the number j of decomposition layers; decomposing the noisy signal to obtain a group of wavelet coefficients, specifically comprising:
step S11: selecting n days of a day to be measured, collecting M point loads of the electrical equipment each day, constructing a signal data sample, and obtaining a load characteristic set as X ═ X1,x2,...,xi,...,xNN is the number of load characteristics of the electrical equipment, xiThe ith characteristic in the load characteristic set X is taken as the characteristic;
step S12: a noisy signal is calculated according to the following equation (1):
g(k)＝f(k)+s(k)k＝1,2,3,…,M-1 (1)
wherein k is time, f (k) is real signal, s (k) is noise signal, g (k) is noise-containing signal;
step S13: selecting a corresponding wavelet basis based on the noise-containing signals g (k), and selecting smooth continuous wavelet basis when the noise-containing signals are smooth; when the linear property of the noise-containing signal is stronger, selecting a linear wavelet base;
step S14: decomposing the noise-containing signals g (k) according to a preset decomposition layer number j to obtain a group of wavelet coefficients omegaj,k。
3. The fusion feature canonical load recognition method based on the combination of fast R-CNN and SVM according to claim 1, wherein the step S2: determining a threshold value of each wavelet coefficient of each layer in stages, and performing soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients, wherein the method specifically comprises the following steps:
performing soft threshold function processing on the wavelet coefficient according to a formula (2) to obtain an estimated wavelet coefficient
Figure FDA0003332310640000021
Figure FDA0003332310640000022
Wherein T is a threshold value; when the wavelet coefficient ωj,kIs greater than T, the estimated wavelet coefficients
Figure FDA0003332310640000023
Has an absolute value of | ωj,kl-T, the sign remains unchanged; when the wavelet coefficient ωj,kIs less than or equal to T, the estimated wavelet coefficients
Figure FDA0003332310640000024
Is zero.
4. The fusion feature canonical load recognition method based on the combination of fast R-CNN and SVM according to claim 1, wherein the step S5: constructing a load characteristic curve image based on the normalized reconstruction signal, performing characteristic extraction on the load characteristic curve image by using an Faster R-CNN network to obtain a characteristic diagram, calculating to obtain an interested region, generating an interested region characteristic diagram, generating a characteristic sequence based on the interested region characteristic diagram, obtaining a characteristic sequence with weight by using an attention mechanism, and classifying the characteristic sequence to obtain a final load identification result; wherein the Faster R-CNN network comprises: the method comprises the following steps of extracting a network, suggesting a network in a region, generating a network by a regional characteristic graph and classifying and regressing a detection network, and specifically comprises the following steps:
step S51: and constructing a load characteristic curve image based on the normalized reconstruction signal: inputting the load characteristic curve image into the characteristic extraction network to obtain a characteristic diagram;
step S52: inputting the characteristic diagram into a region suggestion network (RPN), and calculating a target region to be identified on the characteristic diagram to obtain a region of interest;
step S53: inputting the characteristic diagram of the region of interest into a region characteristic diagram generation network, generating the characteristic diagram of the region of interest by using ROI pooling, and obtaining a characteristic sequence through convolutional layer calculation
Figure FDA0003332310640000025
Computing the sequence of features using an attention mechanism
Figure FDA0003332310640000026
Obtaining a weighted characteristic sequence y ═ y1,y2,...,yt,...,yN]The method comprises the following specific steps:
according to the following formula (3), a query vector q ═ q is calculated1,q2,...,qi,...,qN]The key vector m ═ m1,m2,...,mi,...,mN]Sum vector v ═ v1,v2,...,vi,...,vN]：
Figure FDA0003332310640000031
Wherein, Wq、WmAnd WvIs a preset weight matrix coefficient;
according to equation (4), each is calculated
Figure FDA0003332310640000032
Attention score of ct,iI.e. at present
Figure FDA0003332310640000033
Correlation between the power value at time t and the power values of other vectors in the signature sequence;
ct,i＝qt·mi (4)
normalizing the attention score according to equation (5):
Figure FDA0003332310640000034
the normalized attention scores are weighted and summed according to equation (6):
Figure FDA0003332310640000035
wherein, ytWeighted sequence y ═ y for output after attention mechanism calculation1,y2,...,yt,...,yN]The value at time t;
step S54: inputting the weighted sequence into a classification and regression detection network, and classifying the weighted sequence by using a one-to-one method Support Vector Machine (SVM), wherein the classification specifically comprises the following steps:
according to the weighted sequence y ═ y1,y2,...,yt,...,yN]The load recognition method comprises the steps of classifying the load by using an SVM (support vector machine) to obtain z classes, judging the z classes by using z (z-1)/2 classifiers, voting for the corresponding classes, and taking the class with the largest vote as a final load recognition result.
5. A fusion characteristic typical load recognition system based on combination of fast R-CNN and SVM is characterized by comprising the following modules:
the wavelet coefficient acquisition module is used for selecting n days to be measured, acquiring M point loads of the electrical equipment each day, constructing a signal data sample to obtain a load characteristic set, selecting a corresponding wavelet base according to the load characteristics and determining the decomposition layer number; decomposing the noisy signals to obtain a group of wavelet coefficients;
the wavelet coefficient estimation module is used for determining a threshold value of each wavelet coefficient in stages and performing soft threshold function processing on the wavelet coefficients to obtain estimated wavelet coefficients;
the reconstructed signal module is used for reconstructing the wavelet by using the estimated wavelet coefficient to obtain a de-noised reconstructed signal;
the normalization module is used for performing normalization processing on the reconstruction signal to obtain a normalized reconstruction signal;
the load identification module is used for constructing a load characteristic curve image based on the normalized reconstruction signal, extracting the characteristics of the load characteristic curve image by using an Faster R-CNN network to obtain a characteristic diagram, calculating to obtain an interested area, generating an interested area characteristic diagram, generating a characteristic sequence based on the interested area characteristic diagram, obtaining the characteristic sequence with the weight by using an attention mechanism, and classifying the characteristic sequence to obtain a final load identification result; wherein the Faster R-CNN network comprises: the method comprises the steps of a feature extraction network, a regional suggestion network, a regional feature map generation network and a classification and regression detection network.
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