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Method for on-line predicting static voltage stability margin of parallel sequential limit learning machine

A technology of static voltage stability and extreme learning machine, applied in forecasting, data processing applications, biological neural network models, etc., can solve problems that are difficult to apply to power systems, and achieve real-time effects

Active Publication Date: 2019-02-12
NORTHEASTERN UNIV
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Problems solved by technology

The traditional extreme learning machine adopts the method of batch learning. Whenever new data enters the neural network, it needs to retrain the historical data, which is difficult to apply to the actual power system.

Method used

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  • Method for on-line predicting static voltage stability margin of parallel sequential limit learning machine
  • Method for on-line predicting static voltage stability margin of parallel sequential limit learning machine
  • Method for on-line predicting static voltage stability margin of parallel sequential limit learning machine

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Embodiment Construction

[0049] For explaining technical content of the present invention, achieved purpose and effect in detail, below in conjunction with appendix figure 1 and specific implementation methods are described in further detail.

[0050] Step 1: PSASP initial value setting: respectively set the initial value of the active load and the initial value of the reactive load of the specified 9 nodes, and set the growth mode of the load: according to the basic value of the active power and reactive power on the load bus according to 0.05% The ratio increases sequentially, and 160 sets of training data are obtained. The basic values ​​of each node are as follows:

[0051]

[0052] Step 2: Preprocess the data, and set the PSASP algorithm:

[0053] A) The conventional power flow method is Newton's method

[0054] B) The method of ill-conditioned power flow is selected according to the transition mode correction method

[0055] C) The leading load point is set as bus 16.

[0056] Such as f...

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Abstract

The present invention discloses a method for on-line predicting the static voltage stability margin of a parallel sequential limit learning machine. By using a parallel structure to train the sequential limit learning machine parallelly, the purposes of rapidly finding out the optimal hidden layer node number satisfying the precision requirement, the historical data length N0 and the Block value of the numbers of the data blocks newly added every time are achieved. The method of the invention can accurately predict the static voltage stability of the power network, and provides the basis for the operation and planning of the power system.

Description

technical field [0001] The invention relates to the field of power system prediction, in particular to a method for predicting static voltage stability margin by using a parallel sequential extreme learning machine. Background technique [0002] With the development of society, the power consumption has increased sharply, and the power system often operates near the stability limit. In recent years, accidents caused by voltage have occurred frequently all over the world, so the research on voltage stability has gradually become the mainstream. In the actual application of the power system, the operator needs to make a judgment on whether the current system is safe. The voltage stability margin can give the staff accurate information about how far the current state of the system is from the voltage collapse point. Therefore, how to quickly and accurately It is very important to accurately calculate the voltage stability margin. [0003] In the actual power system, the train...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/06G06Q50/06
CPCG06N3/061G06Q10/04G06Q50/06
Inventor 王占山柳义鹏刘丕丕施展姚显双
Owner NORTHEASTERN UNIV
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