Machine learning model training method and system for power load identification

A machine learning model and power load technology, applied in machine learning, computing models, instruments, etc., can solve the problem of low power load identification accuracy, and achieve the effect of improving the scope of application, improving accuracy, and high matching accuracy.

Pending Publication Date: 2020-06-05
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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Problems solved by technology

[0005] This application provides a machine learning model training method and system for electric load identification to solve the technical problem that the

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  • Machine learning model training method and system for power load identification
  • Machine learning model training method and system for power load identification
  • Machine learning model training method and system for power load identification

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[0059] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described The embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0060] The characteristics of the power load imprint can reflect the unique information reflecting the state of power consumption embodied in the operation of an electrical equipment, such as voltage, active power waveform, current, etc. During the operation of the equipment, these load imprints will appear repeatedly, based on Therefore, we can...

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Abstract

The embodiment of the invention provides a machine learning model training method and system for power load identification. Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network model for continuous optimization; parameters of the model are continuously adjusted by verifying a data set so as to select an optimal model; meanwhile, the performance of the model is evaluated by utilizing the test data set; the optimal effect is achieved; and the model can be further applied to a power load identification system. According to the method, the model can be trained according to the input sampling data, so that the electric equipment in use can be identified according to waveform sampling data of specific voltage, current and active power; therefore, manual parameter adjustment and feature extraction are not needed, and the feature parameters required for identifying the powerload can be autonomously learned and automatically obtained, so that the application range of the model is expanded, and the accuracy of power load identification is improved.

Description

technical field [0001] The present application relates to the technical field of electric load monitoring, in particular to a machine learning model training method and system for electric load identification. Background technique [0002] The power load feature is the rule that the active power and reactive power absorbed by the power load from the power supply of the power system change with the voltage of the load terminal and the system frequency; the power load feature is an important part of the power system; Electrical equipment plays an important role in the development of smart grid technology. [0003] The most commonly used methods for electric load identification are intrusive and non-intrusive identification methods. Among them, the intrusive identification method needs to establish a monitoring system to install sensors to each load. Although this method can directly obtain the measurement data of the load, the installation cost is high, the installation proce...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N20/00
CPCG06Q10/04G06Q10/0639G06Q50/06G06N20/00
Inventor 李波张林山罗永睦周年荣曹敏王浩轩辕哲朱全聪利佳邹京希
Owner YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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