A grid voltage stability prediction method based on power big data

A technology of stability prediction and grid voltage, applied in the field of power engineering, to achieve accurate and rapid prediction results

Active Publication Date: 2018-01-12
HOHAI UNIV
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Problems solved by technology

At present, there is no method that can predict the stability of the grid voltage for a long time, so as to ensure that the grid voltage is always in a stable working state.

Method used

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  • A grid voltage stability prediction method based on power big data
  • A grid voltage stability prediction method based on power big data
  • A grid voltage stability prediction method based on power big data

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Embodiment Construction

[0023] The technical solutions of the present invention will be described in detail below, but the protection scope of the present invention is not limited to the embodiments.

[0024] Such as figure 1 As shown, the present invention provides a method for predicting grid voltage stability based on electric power big data, which specifically includes the following steps:

[0025] Step 1: Combining with the bifurcation theory, establish the judgment index of voltage stability;

[0026] Build a power grid simulation model, such as figure 2 As shown, an IEEE 14-node system simulation model is established in this embodiment.

[0027] The λ-V balance point curve of the system and the saddle node bifurcation point on the curve are obtained after bifurcation calculation of the system model by using the continuum power flow method; the load parameter when the saddle node bifurcation point appears is regarded as the maximum load parameter λ max . Such as image 3 As shown, it show...

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Abstract

The invention discloses a grid voltage stability prediction method based on electric power big data. The present invention first analyzes the voltage stability of the power grid in combination with the bifurcation theory, and finds that the saddle node bifurcation point has a very close relationship with the stability of the power grid voltage, and takes this as one of the indicators for judging the voltage stability of the power grid. Then use big data technology to carry out effective data mining on the data obtained from the power grid. Using the improved BP neural network based on the particle swarm optimization algorithm to establish the nonlinear mapping relationship between the power data and the maximum load parameters, so as to achieve a predictive effect, that is, to predict the maximum load parameters according to the current operating state of the power grid. Combined with the current load parameters of the grid, the voltage stability margin is calculated to judge the stability of the grid voltage. Further, according to the prediction results, determine the regulation and control scheme of the grid voltage, increase the stability margin, enhance the stability of the voltage, and achieve the purpose of long-term safe and stable operation of the grid.

Description

technical field [0001] The invention belongs to the technical field of electric power engineering, and in particular relates to a power grid voltage stability prediction method based on electric power big data. Background technique [0002] Big data technology first appeared in industries such as the Internet, telecommunications, and finance, and usually refers to data volumes above 10TB. Big Data is the ability to capture, manage, and process this data at a scale or complexity beyond that of commonly used technologies if operationalized at reasonable cost and timeframes. The research technology of big data has risen to the will of the country. The scale of data owned by a country and the ability to use data will become an important part of the comprehensive national strength. The possession and control of data will also become the focus of competition between countries and enterprises. Multinational giants such as IBM, Microsoft, Google, and Amazon have gained stronger com...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00
Inventor 李升卫志农袁东栋孙国强
Owner HOHAI UNIV
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