Non-invasive load identification method based on V-I trajectory diagram and neural network

Abstract

The invention discloses a non-intrusive load identification method based on a V-I trajectory diagram and a neural network. The method comprises steps of home-entry voltage and current data and activepower data being acquired in real time; judging whether a load switching event exists or not and whether a load operation state reaches a stable state or not according to the change of the active power; obtaining voltage and current data and power data of the load according to the steady-state voltage and current data before and after the event; a V-I track being converted into an RGB color imagecontaining information such as voltage and current phase difference and power by adopting a simple image processing technology. After an RGB color image is obtained, normalization processing is carried out; and load identification is carried out through a pre-trained convolutional neural network. Compared with the prior art, the method is advantaged in that the steady-state characteristics of theload are fully extracted through the convolutional neural network, and the neural network model can be directly operated on the embedded equipment without depending on the operation support of a server.

Description

technical field [0001] The invention relates to the field of non-intrusive load monitoring (NILM), in particular to a non-intrusive load identification method based on a V-I trajectory diagram and a neural network. Background technique [0002] There are two main types of load identification methods: intrusive load identification and non-intrusive load identification. Although the identification results of intrusive load identification methods are more accurate, they are not popular due to high cost. Non-intrusive load monitoring (non-intrusive load monitoring, NILM has low cost and strong practicability, so NILM has become a hot spot in the field of smart metering of power systems today. By installing an embedded non-intrusive power identification module on the household electricity meter, Then through the load identification algorithm to detect the load working conditions in the building. Combined with effective power management, power saving and energy saving can be achi...

AI Technical Summary

Problems solved by technology

Some load identification methods use the V-I trajectory characteristics in the steady state of the load to identify the load, but do not make full use of the power characteristics of the load

There are also some methods that only use some current harmonic components and the power characteristics of the load in the steady state, and do not make full use of the V-I trajectory characteristics

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