Non-intrusive load identification power decomposition method and system based on priority allocation

Abstract

Non-intrusive load identification power decomposition method and system based on priority distribution, including load start-stop edge detection, load start-stop edge classification, first priority load power decomposition, second priority load power decomposition; according to non-intrusive load The identification equipment collects and calculates the high-frequency sampling information, uses the maximum likelihood ratio algorithm to detect the start-stop edge of the load, and obtains the edge active power, reactive power, and harmonic characteristic information; then calculates the Euclidean distance of the edge characteristic data, and performs load edge Classification, so as to realize the identification of load types; finally, according to the two-step priority, the electric quantity is decomposed separately. The invention executes different electric power decomposition strategies based on the order of priority, solves the problem of inaccurate electric power identification of power fluctuation characteristics such as air conditioners, and can realize accurate electric power decomposition under the simultaneous working state of multiple electric appliances.

Description

technical field [0001] The present invention relates to the technical field of non-intrusive load identification, and more specifically, to a non-intrusive load identification power decomposition method and system based on priority allocation. Background technique [0002] For a long time, the information perception ability of user-side smart energy meters and other measurement equipment is weak, unable to provide accurate fine-grained load data and analysis services, and it is difficult to support two-way information interaction between power companies and residential users. [0003] Non-intrusive load identification technology is a new technology for real-time monitoring of residential power consumption status. It uses high-frequency sampling data from distributed terminals, smart energy meters and other equipment, and applies signal analysis and machine learning algorithms to obtain residential power consumption in a more precise manner. Information, such as the start and...

AI Technical Summary

Problems solved by technology

However, there are few studies on non-intrusive load power decomposition. In the prior art, the Chinese invention patent application (CN111753968A) "Non-intrusive load monitoring smart meter and power decomposition method" adopts sequence-to-point power decomposition with attention mechanism Model, through the self-learning weight fine total power decomposition, and the Chinese invention patent application (CN109840691A) constructed a sub-item power decomposition estimation method based on deep neural network

[0005] Whether it is a deep neural network or a sequence-to-point power decomposition model, the large amount of training sample data necessary for the model is difficult to obtain in practical applications

There are huge differences in the types of electrical appliances in different households, and the update and iteration of the same type of electrical appliances will cause a sharp increase in the number of training samples, otherwise it will lead to a decline in recognition accuracy

At the same time, this type of model must go through a long training and learning process, which is time-consuming, and is not suitable for localized load identification equipment that collects real-time processing and analysis in real time

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