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Analysis model construction method for typical characteristics of wind power output

A technology of wind power output and construction method, applied in wind power generation, electrical components, circuit devices, etc., can solve the lack of systematic wind power output characteristic index system, power workers can not better grasp the wind power output law, limit grid acceptance Wind power capacity, etc.

Inactive Publication Date: 2018-08-07
XINJIANG UNIVERSITY
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Problems solved by technology

[0002] With the development of wind power technology, wind power is currently the fastest growing renewable energy in the world, and the proportion of installed capacity in the power grid is increasing day by day. Due to the characteristics of peak shaving, the impact of large-scale wind power grid connection on the grid is becoming more and more obvious, such as the impact on grid security and stability, wind power transmission, frequency peak shaving, power quality, backup arrangements, many operating units, and coordination difficulties. , not only affects the safe operation of the power grid, but also limits the ability of the power grid to accept wind power, leading to a further increase in the amount of abandoned wind, resulting in energy waste
[0003] Due to the intermittent uncertainty of wind power output, the characteristics of wind power output are different from the load demand, and it is difficult to obtain intuitively by using conventional output indicators (daily maximum / minimum value, daily average value, daily peak-to-valley difference, daily peak-to-valley difference rate, etc.) Outline of wind power output characteristic curve
The evaluation index system of wind power output characteristics is the basis of wind power grid-connected research. At present, most of the research on wind power output characteristics is qualitative analysis, and there is a lack of systematic wind power output characteristic index system, which makes it difficult to deeply grasp the laws and typical characteristics of large-scale wind power output. Characteristics, lack of model construction methods for typical characteristics of wind farm output, resulting in power workers unable to better grasp the law of wind power output, real-time dispatching of power systems, dispatching planning arrangements, wind farm planning and system backup configuration lack of standards

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  • Analysis model construction method for typical characteristics of wind power output
  • Analysis model construction method for typical characteristics of wind power output
  • Analysis model construction method for typical characteristics of wind power output

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Embodiment Construction

[0058] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0059] A method for constructing an analysis model of typical characteristics of wind power output, said construction method comprising the following steps:

[0060] 1) Obtain sample data of wind power output, calculate wind power output characteristic evaluation index and spatial characteristic index and normalize them;

[0061] 2) According to the peak and valley periods of the electricity price table, the sample data of wind power output is aggregated and dimensionally reduced;

[0062] 3) For the segmented data set, initialize the parameters of the fuzzy clustering algorithm to obtain the initial cluster center and fuzzy matrix;

[0063] 4) According to the adaptive function α(c) of the number of clusters, iteratively calculate, find out the largest α(c), and obtain the best fuzzy cluster number c;

[0064] 5) According to the op...

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Abstract

The invention discloses an analysis model construction method for typical characteristics of the wind power output. Original data are obtained and data pretreatment is carried out, the data are normalized by using multi-temporal-scale wind power volatility and simultaneous rate valuation indexes, and segmented aggregation is carried out to reduce the dimension; on the basis of an improved adaptivefuzzy clustering algorithm, an adaptive function alpha (c) is calculated and an optimal cluster number c is determined, and wind power output characteristics are classified; a variation dispersion coefficient betai is introduced and wind-power output distortion data are rejected; and a partition weighted median probability estimation method is put forward and a wind-power output daily typical characteristic curve / zone band is identified and extracted. According to the invention, the multi-space-scale large-scale wind power output characteristics and monthly, quarterly or yearly multi-time-scale wind-power output characteristics are analyzed and calculated and the wind-power output daily typical characteristic curve / zone band is extracted, so that the power operation staff can master the wind-power output rule well and a technical reference is provided for real-time scheduling of a power system, scheduling plan arrangement, wind farm planning and system backup configuration.

Description

technical field [0001] The invention relates to the technical field of electric power system control, in particular to a method for constructing a wind farm output daily typical characteristic curve / interval band analysis model. Background technique [0002] With the development of wind power technology, wind power is currently the fastest growing renewable energy in the world, and the proportion of installed capacity in the power grid is increasing day by day. Due to the characteristics of peak shaving, the impact of large-scale wind power grid connection on the grid is becoming more and more obvious, such as the impact on grid security and stability, wind power transmission, frequency peak shaving, power quality, backup arrangements, many operating units, and coordination difficulties. , not only affects the safe operation of the power grid, but also limits the ability of the power grid to accept wind power, leading to a further increase in the amount of abandoned wind, re...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q10/06G06Q50/06H02J3/38H02J3/48
CPCH02J3/386H02J3/48G06Q10/06393G06Q50/06H02J2203/20G06F18/23211Y02E10/76
Inventor 蔺红徐邦恩刘洋候卫萍
Owner XINJIANG UNIVERSITY
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