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Non-invasive load monitoring method

A load monitoring, non-intrusive technology, applied in the field of power system, can solve the problem of investing a lot of manpower, and achieve the effect of improving effectiveness and robustness

Pending Publication Date: 2020-06-12
SHENZHEN POWER SUPPLY BUREAU
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Problems solved by technology

However, in the existing technology, pure semi-supervised machine learning has not yet shown its advantages, and it still needs to invest a lot of manpower in the learning process to mark

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Embodiment Construction

[0046] The following descriptions of various embodiments refer to the accompanying drawings to illustrate specific embodiments in which the present invention can be implemented.

[0047] The invention automates the learning process of the load pattern in the non-invasive load monitoring by designing a group of new wavelets and by means of collaborative training. In the actual production process, the power data monitored at the main power distribution line is also a time-series signal. Semi-supervised machine learning can establish internal correlations based on the characteristics of historical input data, and as a classifier, realize the classification of power loads. non-intrusive decomposition. In addition, in the designated home environment, the type and quantity of electrical equipment required for the training and identification of the semi-supervised machine learning model can be known. Therefore, in the field of electric load identification, the semi-supervised learnin...

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Abstract

The invention relates to a non-intrusive load monitoring method. The method comprises the following steps: S1, constructing a load monitoring model by using a computer aided design tool of a power system or electromagnetic transient analysis containing direct current; s2, obtaining an identified load and dissimilarity measurement value and an identified wavelet high-pass filter coefficient by adopting wavelet design and a general analysis method; s3, performing cooperative training of machine learning by adopting a decision tree and a nearest neighbor classifier according to results of the step S1 and the step S2; and S4, verifying the training result in the step 3 by adopting a K-layer cross test and a Monte Carlo method, and optimizing the load monitoring model. The method is high in robustness and free of noise interference, and the influence of noise pollution on signals is reduced.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a non-invasive load monitoring method. Background technique [0002] With the advent of the energy Internet era, clean power generation, efficient power distribution, and convenient power consumption have brought new changes to the smart grid. Non-intrusive electric load monitoring system (NILMS) is a relatively cutting-edge technology for monitoring the working conditions of electrical equipment. It does not need to install sensing equipment "near the load", and only monitors and analyzes the power distribution incoming line. The voltage, current and other signals of the user can be used to obtain the internal load characteristics of different types of loads. This technology will show its excellent performance in the fields of improving the accuracy of load forecasting, helping users optimize power consumption and energy saving, assisting power theft supervision and equip...

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Application Information

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IPC IPC(8): G06K9/62G06Q50/06H02J3/00
CPCG06Q50/06H02J3/00G06F18/24147G06F18/24323G06F18/214
Inventor 李锐史帅彬林小红
Owner SHENZHEN POWER SUPPLY BUREAU
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