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Power system transient stability assessment method based on differential evolution extreme learning machine

A technology of transient stability evaluation and extreme learning machine, which is applied in electrical digital data processing, computer-aided design, design optimization/simulation, etc., and can solve problems such as unstable network results

Inactive Publication Date: 2019-07-02
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

In the execution process of the extreme learning machine, only the number of hidden layer nodes of the network needs to be set, and the optimal solution can be obtained with an efficient learning rate without adjusting the randomly generated input layer weights and hidden layer thresholds. lead to unstable network results

Method used

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  • Power system transient stability assessment method based on differential evolution extreme learning machine
  • Power system transient stability assessment method based on differential evolution extreme learning machine
  • Power system transient stability assessment method based on differential evolution extreme learning machine

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Embodiment Construction

[0026] Step 1: Simulate the faults in the typical operation mode of the power system through the time domain simulation method to obtain the original simulation data;

[0027] Step 2: Perform data normalization processing on the sample set;

[0028] Step 3: Label the sample data according to the transient stability index, the stable sample is marked as 1, and the unstable sample is marked as 0;

[0029] Step 4: Construct a transient stability evaluation model based on differential evolution extreme learning machine, and divide the marked data set in step 3 into training set / test set according to 8:2, each subset is selected from the total sample set by uniform random sampling Take it out to ensure that the ratio of stable / unstable samples is consistent with the overall sample;

[0030] Step 5: Use the training set and test set constructed in step 4 to optimize the transient stability evaluation model in step 4 with the evaluation accuracy as the standard, and save the model w...

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Abstract

The invention relates to a power system transient stability assessment method based on a differential evolution extreme learning machine technology. The method comprises the following steps: firstly,simulating fault data under various typical operation modes by using electric power system simulation software; extracting electrical quantity feature from the simulation data, and then inputting theoptimal feature set into the differential evolution limit learning machine for transient stability evaluation. According to the invention, compared with other types of extreme learning machine models,the method has the advantages that the evaluation accuracy is higher, and the method is of great significance to online safety and stability evaluation of the power system.

Description

technical field [0001] The invention relates to transient stability evaluation of a power system, in particular to a transient stability evaluation method of a power system based on a differential evolution extreme learning machine. [0002] technical background [0003] The ever-expanding system scale of the modern power system has brought severe challenges to the dispatching operation. The increasingly complex network structure and more and more important loads make the safe and stable operation of the system even more important. Power system transient stability focuses on the stability of the system after severe faults, which is the most basic problem in various stability studies. However, in recent years, severe power outages have occurred many times around the world, causing huge economic losses and social impacts. At present, the traditional transient stability assessment methods mainly include time-domain simulation method and energy function method. By solving the c...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 李向伟许刚刘向军
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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