SFR and ELM based power grid transient state frequency characteristic prediction method

A technology of frequency characteristics and power grid transients, applied to electrical components, circuit devices, AC network circuits, etc., can solve problems such as reduced calculation accuracy, achieve the effect of improving accuracy and reliability, and reducing risks

Inactive Publication Date: 2018-05-25
SOUTHEAST UNIV
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Problems solved by technology

Due to the refinement of the analysis of the complex nonlinear model in the frequency dynamic process, the accuracy of the frequency dynamic feature prediction is determined. When the above SFR method is applied online, the complex nonlinear model is greatly simplified to improve the speed, but it also leads to a significant increase in the calculation accuracy. reduce

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  • SFR and ELM based power grid transient state frequency characteristic prediction method
  • SFR and ELM based power grid transient state frequency characteristic prediction method
  • SFR and ELM based power grid transient state frequency characteristic prediction method

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Embodiment Construction

[0025] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0026] figure 1 It is a framework diagram of an online prediction model for power grid transient frequency characteristics proposed by the present invention. The main factors that determine the frequency response characteristics of the system after being disturbed in the power grid are the inertia, damping and speed regulation characteristics of the system composed of generator sets and loads. In the present invention, the SFR model is established based on this; while the grid structure, voltage and reactive power dynamic characteristics, etc. It also has a slight impact on the system frequency situation, and the present invention establishes an ELM-based error correction model based on this. Disturbance event description, power system topology / parameters, state data and other information are used as the input of the SFR model; the measuremen...

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Abstract

The invention provides an SFR and ELM based power grid transient state frequency characteristic prediction method, and belongs to the technical field of power grid security and stability analysis. According to the method, a system frequency response model and a limit learning machine theory are adopted as support, a power grid transient state frequency characteristic prediction model is built, theinfluence on the communication abnormal scene is considered, the system online measured data can be depended on, and frequency change characteristic prediction in the power grid transient state process is achieved. On the premise that the requirement for online computation speed is met, the power grid transient state prediction characteristic result accuracy and reliability are ensured, and therefore the decision basis is provided for power system state evaluation and operation control.

Description

technical field [0001] The invention relates to a method for analyzing the safety and stability of a power grid, in particular to an online prediction method for the transient frequency characteristics of a power grid. Background technique [0002] With the advancement of the construction of large-scale UHV AC-DC transmission channels, the increase in the proportion of power received has indirectly reduced the frequency regulation capability of traditional power sources in the receiving-end grid, leading to an increased risk of abnormal fluctuations in grid frequency in the case of large power shortages. Therefore, high-precision online prediction of the power grid frequency situation after the system is disturbed is of great significance to ensure the frequency stability of the receiving end power grid. [0003] The traditional power system transient frequency stability analysis methods mainly include the full-time domain simulation method by solving high-order nonlinear di...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/00
CPCH02J3/00H02J2203/20Y02E40/70Y02E60/00Y04S10/00Y04S10/22
Inventor 王琦李峰汤奕
Owner SOUTHEAST UNIV
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