On-line calculation method of probabilistic power flow based on BP neural network

A BP neural network, probabilistic power flow technology, applied in the field of power system and its automation, can solve problems such as speeding up the power flow solution speed and difficult online analysis

Active Publication Date: 2019-01-01
CHONGQING UNIV
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Problems solved by technology

Most of the improved iterative algorithms are based on Newton's method, such as fast decoupling method, quasi-Newton method, etc., which speed

Method used

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  • On-line calculation method of probabilistic power flow based on BP neural network
  • On-line calculation method of probabilistic power flow based on BP neural network
  • On-line calculation method of probabilistic power flow based on BP neural network

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0069] see figure 1 , a probabilistic power flow online calculation method based on BP neural network, which mainly includes the following steps:

[0070] 1) Establish a BP neural network power flow model.

[0071] Further, the BP neural network is a multilayer feed-forward network trained by the error backpropagation algorithm. The BP neural network power flow model includes an input layer, a hidden layer and an output layer.

[0072] The number of neurons in the input layer is set to n. Any neuron in the input layer is denoted as x i , i=1, 2...n.

[0073] The number of neurons in the hidden layer is 1.

[0074] The number of neurons in the output layer is set to m. Any neuron in the output layer is denoted as d e , e=1, 2...m.

[0075] The values ​​of n and m are determined by the scale and complexity of the power system.

[0076] 2) Initialize the basic parameters of the BP neural network power flow model, mainly including: the weight W from the input layer to the...

Embodiment 2

[0127] An experiment of applying the probabilistic power flow online calculation method based on BP neural network to the power system mainly includes the following steps:

[0128] 1) Establish a BP neural network power flow model.

[0129] 2) Initialize the basic parameters of the BP neural network power flow model, mainly including: the weight W from the input layer to the hidden layer ij and the weight W from the hidden layer to the output layer je and learning rate η.

[0130] In this embodiment, the training sample input and the training sample input are divided into 1000 small batches. After removing the elements with the same input data, the remaining 189 elements, the number of neurons in the hidden layer can be 200, the learning rate is initially 0.8, after 500 iterations, the learning rate decays to 0.1, and the momentum factor is 0.5.

[0131] 3) Obtain training sample data. The training sample data mainly includes wind speed, irradiance and load of the power sy...

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Abstract

The invention discloses an on-line calculation method of probability power flow based on BP neural network, which mainly comprises the following steps: 1) establishing a BP neural network power flow model. 2) initializing the basic parameters of BP neural network power flow model. 3) acquiring training sample data. 4) identifying training objectives; using the training sample data, the BP neural network power flow model being trained to obtain the trained BP neural network power flow model. 5) obtaining a calculation sample. 6) inputting the training sample data obtained in the step 3 into theBP neural network power flow model trained in the step 4 at one time to obtain the training target, thereby judging the solvability of the power flow of all the training samples; calculating the tidal current value of the solvable sample. 7) performing statistic probability power flow index. That invention can be widely apply to on-line calculation of probability power flow of a power system, andis particularly suitable for the situation that the uncertainty of the power system is enhance due to high proportion access of new energy sources.

Description

technical field [0001] The invention relates to the field of electric power system and automation thereof, in particular to an online calculation method of probability power flow based on BP neural network. Background technique [0002] Power systems essentially operate in uncertain environments. Probabilistic power flow can take into account the influence of uncertain factors, obtain the probability characteristics of system state variables, and use it in power system planning and operation. In recent years, due to the increasing penetration of renewable energy such as photovoltaics and wind power, the uncertainty of the power system has surged. In order to meet the requirements of power system operation and scheduling, the demand for online probabilistic power flow calculation is becoming more and more urgent. [0003] At present, the probabilistic power flow solution methods mainly include analytical method and simulation method. Analytical methods (convolution method,...

Claims

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/084
Inventor 余娟任鹏凌郭林严梓铭杨燕向明旭
Owner CHONGQING UNIV
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