Deep learning-based load modeling and online correction method

A technology of load modeling and deep learning, applied in character and pattern recognition, data processing applications, instruments, etc., can solve problems such as high dependence on fault data and modeling algorithms that cannot be applied online

Active Publication Date: 2019-12-20
TIANJIN UNIV +1
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is: based on the basic theory of load modeling and online correction of load model parameters based on WAMS measurement data, a method of power load modeling and online correction that can be applied in the case of complex power systems is provided, which overcomes the The shortcomings of the traditional modeling method, which are highly dependent on fault data and the modeling algorithm cannot be applied online, provide load model preparation for power grid operators to perform simulation calculation and analysis

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  • Deep learning-based load modeling and online correction method

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Embodiment Construction

[0020] A load modeling and online correction method based on deep learning, comprising the following steps:

[0021] Step 1. Obtain the historical data samples of the load nodes to be identified, cluster the data samples according to the simulation calculation input variables, select the samples closest to the cluster center in each category as typical samples, and calculate the relationship between each sample in the same category and Variation in a typical sample;

[0022] The acquired node historical data includes active power P, reactive power Q, node voltage U and bus frequency f of load nodes. Among them, P and Q are the input quantities of the simulation calculation, U and f are the output quantities of the simulation calculation. When clustering, the continuously changing P and Q waveform curves within 15 minutes were used as samples for clustering, and the clustering method used the Mini Batch K-Means algorithm. The specific steps of the algorithm can be found in re...

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Abstract

The invention discloses a deep learning-based load modeling and online correction method, which includes: (1) clustering historical sample data and calculating a variable quantity; (2) constructing anoffline simulation sample and calculating a simulation output variable quantity; (3) calculating an association rule between input and output variable quantities and a load model parametric variationby using a deep learning network; and (4) acquiring online data and online correcting the model. Based on a conventional power system load model research, the method, by using the idea of offline learning and online application and based on the deep learning, solves the construction and online correction of a complex power system load model in the context of a smart grid. The method provides a feasible solution for the construction and online correction of the power system load model, further improves the accuracy and the calculation accuracy of power system simulation model under a complex condition, and provides new research ideas for subsequent power system load modeling research.

Description

technical field [0001] The present invention relates to a load modeling and online correction method based on deep learning, in particular to a load modeling and online correction method based on deep learning. Background technique [0002] As one of the important components in the power system, the load has a great influence on the analysis and simulation calculation of the static, dynamic and transient characteristics and stability of the power system. However, the widely used load models are still relatively oversimplified and rough, such as static load models such as constant impedance and power. The excessive roughness of the load model has become a key factor restricting the accuracy of power system analysis and simulation calculations. It is of great practical significance to establish a dynamic load model that is realistic and can accurately reflect the important characteristics of the actual situation. [0003] With the continuous development of the power system, t...

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Application Information

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IPC IPC(8): H02J3/00G06K9/62G06Q50/06
CPCH02J3/00G06Q50/06G06F18/23213Y04S10/22Y02E60/00
Inventor 曾沅贾凡秦超孙冰刘博
Owner TIANJIN UNIV
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