RBF neural network-based probability power flow online calculation method

A probabilistic power flow, neural network technology, applied in the field of power system and its automation, can solve problems such as speeding up the power flow solution speed, difficult online analysis, etc., to achieve the effect of high-precision online calculation, improved accuracy, and good parallelism

Active Publication Date: 2018-07-27
CHONGQING UNIV
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  • Application Information

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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 up the power flow solution to a certain extent, but iterative calculation is still required, so it is difficult to be used for online analysis

Method used

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  • RBF neural network-based probability power flow online calculation method
  • RBF neural network-based probability power flow online calculation method
  • RBF neural network-based probability power flow online calculation method

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Experimental program
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Effect test

Embodiment 1

[0065] A probabilistic power flow online calculation method based on RBF neural network mainly includes the following steps:

[0066] 1) Establish the RBF neural network probability power flow model.

[0067] Further, an RBF neural network probability power flow model mainly includes an input layer, a hidden layer and an output layer.

[0068] The data in the input vector X of the input layer mainly includes active power and reactive power of all new energy nodes and load nodes in the power system.

[0069] The hidden layer uses RBF Kernel to make nonlinear transformation on the input, so that the output layer can train the linear classifier.

[0070] The data in the output vector y of the output layer mainly includes power flow solvability, node voltage active power, node voltage reactive power, branch active power and branch reactive power.

[0071] The number of nodes in the input layer is set to N. The number of nodes in the hidden layer is I. The nodes of the output l...

Embodiment 2

[0124] An experiment for calculating the probability power flow of a power system using an RBF neural network-based probabilistic power flow online calculation method mainly includes the following steps:

[0125] 1) Establish the RBF neural network probability power flow model.

[0126] 2) Obtain the training samples x of the RBF neural network probabilistic power flow model by monitoring the power system in real time, simulating and experimenting the power system, record the power flow values ​​of all training samples x, and mark the unsolvable training samples of the power flow.

[0127]The basic data of the system in this embodiment refers to the IEEE118 standard system. It is assumed that the random characteristics of the loads of each node obey the normal distribution, and its standard deviation is 10% of the expected value of the load of each node; the wind speed obeys the two-parameter Weibull distribution, and the scale parameter is 2.016 , with a shape parameter of 5....

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Abstract

The invention discloses an RBF neural network-based probability power flow online calculation method. The calculation method mainly comprises the following steps of 1) establishing an RBF neural network probability power flow model; 2) obtaining a training sample x of the RBF neural network probability power flow model; 3) performing processing on data x of the training sample; 4) performing training on the RBF neural network probability power flow model; 5) obtaining a calculation sample; 6) inputting the calculation sample data obtained in the step 5 into the RBF neural network probability power flow model completely trained in the step 4 in one time to obtain a training object, so as to judge the power flow solvability of the training sample, calculating the power flow value of the solvable sample, and performing counter normalization processing on the calculation sample data; and 7) performing statistics on a probability power flow index. The calculation method can be widely applied to probability power flow online calculation of the power system, particularly suitable for a condition of the power system with reinforced uncertainty caused by high proportion access of new energies.

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 RBF 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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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/06
CPCH02J3/06H02J2203/20
Inventor 余娟郭林严梓铭任鹏凌杨燕向明旭
Owner CHONGQING UNIV
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