CN113393121A - Non-invasive load identification method based on load power fingerprint characteristics - Google Patents
Non-invasive load identification method based on load power fingerprint characteristics Download PDFInfo
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- CN113393121A CN113393121A CN202110658492.3A CN202110658492A CN113393121A CN 113393121 A CN113393121 A CN 113393121A CN 202110658492 A CN202110658492 A CN 202110658492A CN 113393121 A CN113393121 A CN 113393121A
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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Abstract
The invention discloses a non-invasive load identification method based on load power fingerprint characteristics, which comprises the following steps of 1, collecting power fingerprint characteristic data of a load; step 2, carrying out standardization processing on the characteristic data, and dividing the processed data into a training set and a verification set; step 3, converting the training set data into an input matrix, and establishing and training a convolutional neural network based on an attention mechanism; step 4, judging the accuracy of the model through the verification set; step 5, using the user data for identification test; the load identification method solves the technical problems that the load identification method in the prior art can not be applied to the whole power equipment, especially the load identification method can not effectively identify the distributed power generation and energy storage equipment, and has the defects of not wide application scenes and service scenes and the like.
Description
Technical Field
The invention belongs to a load identification technology, and particularly relates to a non-invasive load identification method based on load power fingerprint characteristics.
Technical Field
The load identification technology in the prior art is generally based on the traditional power fingerprint characteristics for identification, the power fingerprint characteristics are only limited to load equipment and cannot be applied to the whole power equipment, and particularly, the load identification technology cannot effectively identify distributed power generation and energy storage equipment, and has the defects of not wide application scenes and service scenes and the like.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the non-intrusive load identification method based on the load power fingerprint characteristics is provided to solve the technical problems that the load identification method in the prior art cannot be applied to the whole power equipment, particularly the load identification method cannot effectively identify distributed power generation and energy storage equipment, and the application scene and the service scene are not wide.
The technical scheme of the invention is as follows:
a non-intrusive load identification method based on load power fingerprint features comprises the following steps:
step 3, converting the training set data into an input matrix, and establishing and training a convolutional neural network based on an attention mechanism;
step 4, judging the accuracy of the model through the verification set;
and 5, using the user data for identification test.
The power fingerprint feature data includes: active power, reactive power, apparent power, power factor, voltage magnitude, current 0-11 harmonic content values, and voltage 0-11 harmonic content values.
The method for carrying out the normalized processing on the data in the step 2 comprises the following steps: processing is carried out through a normalization processing formula:
a' represents the normalized result, and a represents TettCharacterization data, amaxMaximum value representing the class of characteristic data, aminRepresenting the minimum value of the class of feature data.
The method for converting the training set data into the input matrix comprises the following steps: the training set data is converted to a 28 x 3 input matrix.
The method for training the convolutional neural network based on the attention mechanism comprises the following steps: training a convolutional neural network based on an attention mechanism by using power fingerprint characteristic data of a single load; the attention mechanism refers to the distribution of learning weights, i.e., different portions of the input data or feature map have different concentrations.
4, when the accuracy of the model is judged by using the verification set, if the accuracy of the model meets the requirement, finishing the training of the model; otherwise, adjusting the parameters of the model network.
During the identification test, the electrical data of the user bus is used for the identification test.
During identification testing, detecting the occurrence of a load switching event by adopting a sliding time window algorithm, and differentiating power fingerprint characteristic data before and after the switching event to obtain changes; and converting the change data into an input matrix of a neural network, identifying load power fingerprint characteristics in change by the trained model, separating the load power fingerprint characteristics into a group of power fingerprint characteristics of a single load, and finally realizing non-invasive load identification.
The invention has the beneficial effects that:
(1) the non-invasive load identification method based on the load power fingerprint characteristics has wide application prospect and service scene in practice. Based on the power fingerprint characteristics, a power fingerprint information database containing a large number of power devices can be constructed. And in combination with the information base, each identification link can generate a corresponding business model.
(2) The invention designs a non-invasive load identification method based on load power fingerprint characteristics, which is an application of a power fingerprint technology. Based on the power fingerprint technology and the invention, the industrial and commercial users can conveniently use comprehensive energy services such as demand response and the like, and power grid enterprises can monitor various load information, realize peak load regulation and valley load filling, reduce the power generation cost and realize win-win.
(3) The invention designs a non-invasive load identification method based on load power fingerprint characteristics, adopts a new deep learning solution, and has better performance; the convolutional neural network does not need to manually select features and train weights, namely, the convolutional neural network has a good feature classification effect, and the network has the characteristic of sharing convolutional kernels, so that the processing pressure of high-dimensional data is not needed.
The load identification method solves the technical problems that the load identification method in the prior art can not be applied to the whole power equipment, especially the load identification method can not effectively identify the distributed power generation and energy storage equipment, and has the defects of not wide application scenes and service scenes and the like.
Description of the drawings:
FIG. 1 is a flow chart of a non-intrusive load identification method based on load power fingerprint characteristics according to the present invention;
FIG. 2 is a schematic diagram of the structure of the convolutional neural network model based on the attention mechanism of the present invention.
The specific implementation mode is as follows:
the definition of the power fingerprint of the invention is summarized as follows: by monitoring the electrical data of the power grid equipment, feature points capable of representing certain characteristics of the equipment are mined by using an artificial intelligence technology and a big data technology, and the aggregation of the multidimensional feature points is the power fingerprint characteristics of the equipment. Unlike traditional load signatures, power fingerprint signatures are limited to load devices. The application object of the power fingerprint feature can be expanded to the whole power equipment field. The method can identify the power fingerprint of the user side equipment, and can also identify some distributed power generation and energy storage equipment. The non-invasive load identification is used for decomposing the power load component by recording the total load information of a user bus to obtain the information of each electric device, and further obtaining the energy consumption information of the electric devices and the power utilization rule of users. The economic investment is small, and the user acceptance is high. The power fingerprint technology is a new load identification technology, accurate load characteristic information can be provided, and the combination of the power fingerprint technology and the new load identification technology can well meet the actual requirement of load identification.
A non-intrusive load identification method based on load power fingerprint features comprises the following steps:
step 3, converting the training set data into an input matrix, and establishing and training a convolutional neural network based on an attention mechanism;
step 4, judging the accuracy of the model through the verification set;
and 5, using the electrical data of the user bus for identification test.
The power fingerprint feature data includes: active power, reactive power, apparent power, power factor, voltage magnitude, current 0-11 harmonic content values, and voltage 0-11 harmonic content values.
The method for carrying out the normalized processing on the data in the step 2 comprises the following steps: processing is carried out through a normalization processing formula:
a' represents the normalized result, a represents the characteristic data, amaxMaximum value representing the class of characteristic data, aminRepresenting the minimum value of the class of feature data.
The method for converting the training set data into the input matrix comprises the following steps: the training set data is converted to a 28 x 3 input matrix.
The method for training the convolutional neural network based on the attention mechanism comprises the following steps: training a convolutional neural network based on an attention mechanism by using power fingerprint characteristic data of a single load; the attention mechanism refers to the distribution of learning weights, i.e., different portions of the input data or feature map have different concentrations.
4, when the accuracy of the model is judged by using the verification set, if the accuracy of the model meets the requirement, finishing the training of the model; otherwise, adjusting the parameters of the model network.
During the identification test, the electrical data of the user bus is used for the identification test.
During identification testing, detecting the occurrence of a load switching event by adopting a sliding time window algorithm, and differentiating power fingerprint characteristic data before and after the switching event to obtain changes; and converting the change data into an input matrix of a neural network, identifying load power fingerprint characteristics in change by the trained model, separating the load power fingerprint characteristics into a group of power fingerprint characteristics of a single load, and finally realizing non-invasive load identification.
Claims (8)
1. A non-intrusive load identification method based on load power fingerprint features comprises the following steps:
step 1, collecting power fingerprint characteristic data of a load;
step 2, carrying out standardization processing on the characteristic data, and dividing the processed data into a training set and a verification set;
step 3, converting the training set data into an input matrix, and establishing and training a convolutional neural network based on an attention mechanism;
step 4, judging the accuracy of the model through the verification set;
and 5, using the user data for identification test.
2. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: the power fingerprint feature data includes: active power, reactive power, apparent power, power factor, voltage magnitude, current 0-11 harmonic content values, and voltage 0-11 harmonic content values.
3. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: the method for carrying out the normalized processing on the data in the step 2 comprises the following steps: processing is carried out through a normalization processing formula:
a' represents the normalized result, a represents the characteristic data, amaxMaximum value representing the class of characteristic data, aminRepresenting characteristic data of this typeA minimum value.
4. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: the method for converting the training set data into the input matrix comprises the following steps: the training set data is converted to a 28 x 3 input matrix.
5. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: the method for training the convolutional neural network based on the attention mechanism comprises the following steps: training a convolutional neural network based on an attention mechanism by using power fingerprint characteristic data of a single load; the attention mechanism refers to the distribution of learning weights, i.e., different portions of the input data or feature map have different concentrations.
6. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: 4, when the accuracy of the model is judged by using the verification set, if the accuracy of the model meets the requirement, finishing the training of the model; otherwise, adjusting the parameters of the model network.
7. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: during the identification test, the electrical data of the user bus is used for the identification test.
8. The non-intrusive load identification method based on load power fingerprint features as claimed in claim 1, wherein: during identification testing, detecting the occurrence of a load switching event by adopting a sliding time window algorithm, and differentiating power fingerprint characteristic data before and after the switching event to obtain changes; and converting the change data into an input matrix of a neural network, identifying load power fingerprint characteristics in change by the trained model, separating the load power fingerprint characteristics into a group of power fingerprint characteristics of a single load, and finally realizing non-invasive load identification.
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| TWI802245B (en) * | 2022-01-24 | 2023-05-11 | 台灣電力股份有限公司 | Power consumption analysis system and power consumption analysis method based on non-intrusive appliance load monitoring |
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