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

A neural network and load recognition technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of insufficient utilization of load power characteristics and V-I trajectory characteristics, and achieve good identification results.

A neural network and load recognition technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of insufficient utilization of load power characteristics and V-I trajectory characteristics, and achieve good identification results.

CN112418722AActive Publication Date: 2021-02-26ZHEJIANG UNIV
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  • Non-invasive load identification method based on V-I trajectory diagram and neural network
  • Non-invasive load identification method based on V-I trajectory diagram and neural network
  • Non-invasive load identification method based on V-I trajectory diagram and neural network

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Embodiment Construction

[0031] The present invention is explained in conjunction with the accompanying drawings and the implementation of using the BLUED public data set. The specific implementation steps are as follows:

[0032] The invention provides a non-invasive load recognition method based on V-I trajectory diagram and neural network, such as figure 1 As shown, its implementation steps include:

[0033] S1: First extract 5 types of household electrical equipment from the BLUED dataset, then construct an RGB color map based on V-I trajectory features and train a convolutional neural network model as a recognition network, such as the Alexnet model. The recognition network model in this embodiment is shown in the figure. In order to be able to run directly on the MCU above STM32F7, the recognition network model built is not complicated, including two convolutional layers, two pooling layers and three full layers. The connection layer, the specific structure is as figure 2 shown.

[0034] S2:...

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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...

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Application Information

Patent Timeline
26 Feb 2021
Publication
CN112418722A
IPC
G06Q10/06; G06Q50/06; G06N3/04; G06N3/08
CPC
G06Q10/063114; G06Q50/06; G06N3/08; G06N3/045; Y04S20/242; G06N3/0464; G06N3/09; G01R21/133
Inventors
陆玲霞; 强柱成