Longitudinal time axis clustering method in generalized load modeling on basis of seasonality

A technology of generalized load and clustering method, applied in the field of vertical time axis clustering, which can solve the problems of inability to exclude subjective factors, inability to classify all sample data, and lack of universal applicability.

Active Publication Date: 2014-12-10
SHANDONG UNIV
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Problems solved by technology

The above-mentioned literature can solve the time-varying problem well through a reasonable classification and synthesis method, but the clustering method used in some literature needs to artificially set the number of clusters, cluster centers, etc., which cannot exclude subjective factors, and is not universal in the new situation. Applicability; In addition, due to the small sample s...

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  • Longitudinal time axis clustering method in generalized load modeling on basis of seasonality
  • Longitudinal time axis clustering method in generalized load modeling on basis of seasonality
  • Longitudinal time axis clustering method in generalized load modeling on basis of seasonality

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Embodiment Construction

[0074] The present invention is described in detail below in conjunction with accompanying drawing:

[0075] AP clustering algorithm

[0076] The traditional commonly used Bayesian classification method and distance classification method have simple models, and it is difficult to reflect the intricate relationship between seasons and load operation levels. In addition, it is difficult to cluster large sample data. In recent years, K-means clustering algorithm, fuzzy clustering algorithm, neural network method, etc. are commonly used in the classification and synthesis of load characteristics, but most of them need to be artificially set, such as the number of clusters, cluster centers, etc., and subjective factors cannot be ruled out. This application introduces the AP clustering algorithm. Affinity Propagation Clustering (AP clustering) proposed by Brendan J. Frey et al. is an effective clustering method. This method is an unsupervised clustering method. It is not necessary...

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Abstract

The invention discloses a longitudinal time axis clustering method in generalized load modeling on the basis of seasonality. According to the method, root bus data formed by wind power and loads of the whole year are obtained; transverse time axis pre-clustering is carried out, single day time re-clustering is carried out, longitudinal time unit clustering is carried out, longitudinal time axis clustering results of the whole year are output, a longitudinal time axis clustering method is utilized for analyzing the data of the whole year, and categorical data considering the seasonality are obtained for accurate modeling. An AP algorithm and a longitudinal time axis clustering strategy considering the seasonality are utilized, the large sample data actually measured can be partitioned reasonably, and the simulation result shows that compared with a traditional modeling method, generalized load modeling carried out after clustering analysis makes a model practical on the basis of meeting the requirement for accuracy, and is beneficial for improving the simulation accuracy and the simulation effectiveness of an electrical power system.

Description

technical field [0001] The invention relates to a seasonality-based longitudinal time axis clustering method in generalized load modeling. Background technique [0002] As an intermittent energy source, wind power has a great impact on the security and stability of the power system due to its randomness and volatility, and also brings great challenges to generalized load modeling. With the increase of wind power capacity, the generalized load node sometimes presents power source characteristics and sometimes load characteristics. Different characteristics correspond to different models, which will produce qualitative changes in power system simulation calculation. Power system analysis is very important. [0003] In measurement-based load modeling, time-variation is the biggest obstacle to its application. However, the uncertainty brought about by wind power access increases the difficulty of the original load modeling problem. Research shows that classification and synthe...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 梁军张旭贠志皓
Owner SHANDONG UNIV
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