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Power demand side device identification method and system based on adaptive resonance network

A power demand side, resonant network technology, applied in data processing applications, character and pattern recognition, instruments, etc., can solve the problems of complexity, low recognition efficiency, and inability to update local databases in real time, achieving broad application prospects and improving accuracy. and ease of use

Active Publication Date: 2020-03-17
WUHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the wide variety of unknown devices on the power demand side, the new equipment purchased by users is more complicated, and the brand and model levels are endless. The existing identification methods cannot update the local database in real time, and it is difficult to adapt to the unknown devices newly connected by users. The identification accuracy rate of the unknown device is not high, and the existing technology cannot carry out self-learning and reclassification of the unknown equipment, which leads to the reduction of identification efficiency

Method used

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  • Power demand side device identification method and system based on adaptive resonance network
  • Power demand side device identification method and system based on adaptive resonance network
  • Power demand side device identification method and system based on adaptive resonance network

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Embodiment 1

[0056] Such as figure 1 , this embodiment discloses a method for identifying power demand side equipment based on an adaptive resonant network, including:

[0057] S100. Collect multi-dimensional characteristic data of unknown equipment on the power demand side, and extract unknown equipment load events according to the event detection algorithm, and obtain unknown equipment start-stop time, each characteristic transient variation, and steady-state operation characteristic data through the unknown equipment load events.

[0058] Specifically, a non-intrusive load monitoring device is used to collect the total power data on the user side, including at least multi-dimensional characteristic data such as voltage, current, active power, reactive power, and harmonics within a certain period of time. Taking active power as an example, the original acquisition waveform of electric power data is as follows: figure 2 shown. Then, the events of load state changes are extracted throug...

Embodiment 2

[0091] The present invention also discloses a power demand-side equipment identification system based on an adaptive resonant network applied in Embodiment 1, including: a power data acquisition and detection module 1, a feature input module 2, a learning and training module 3, and an identification module 4 and database matching module 5;

[0092] The power data acquisition and detection module 1 is used to collect multi-dimensional characteristic data of unknown equipment on the power demand side, and extract unknown equipment load events according to the event detection algorithm, and obtain unknown equipment start-stop time, each characteristic transient change amount and Steady-state operating characteristic data;

[0093] The feature input module 2 classifies the start-stop time of the unknown equipment, the transient variation of each feature, and the steady-state operation feature data to obtain input data and test data, and normalizes and encodes the input data and test...

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Abstract

The invention discloses a power demand side device identification method and system based on an adaptive resonance network. The method comprises the following steps of acquiring the multi-dimensionalfeature data of a power demand side unknown device, extracting the load events of the unknown device according to an event detection algorithm, and obtaining the start-stop time, the feature transientvariations and the steady-state operation feature data of the unknown device by utilizing the load events of the unknown device; classifying the feature data to obtain the input data and the test data, and normalizing and encoding the input data and the test data; training and learning the input data through the adaptive resonance network to obtain an adaptive resonance network training model; identifying the test data according to the adaptive resonance network training model to obtain an identification result; and matching the identification result according to a pre-stored database to obtain a matching result of the unknown device. The adaptive resonance network can be trained with the standard data, can carry out the supervised learning, but also can classify the unmarked data and identify the unknown device.

Description

technical field [0001] The invention belongs to the field of non-intrusive load identification, in particular to a power demand side equipment identification method and system based on an adaptive resonant network. Background technique [0002] Non-intrusive load identification technology is currently a research hotspot in the field of power system smart metering, and has developed rapidly in recent years. By installing a non-intrusive power identification device on the power demand side, using intelligent analysis technology, the type, operating status and energy consumption of unknown equipment within the power consumption range can be obtained, thereby realizing real-time monitoring of start-stop and power consumption of different equipment. This technology can help users and power grid companies analyze power consumption behavior on the demand side, and provide strong data support for energy conservation and emission reduction, energy management, smart home management, a...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q50/06
CPCG06Q50/06G06F18/24G06F18/214
Inventor 周洪要若天周东国胡文山邓其军
Owner WUHAN UNIV