Unlock instant, AI-driven research and patent intelligence for your innovation.
On-line Calculation Method of Probabilistic Power Flow Based on bp Neural Network
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A BP neural network and 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, difficult online analysis, etc. Effect
Active Publication Date: 2021-11-16
CHONGQING UNIV
View PDF9 Cites 0 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
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
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more
Image
Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
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 backpropagationalgorithm. 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...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More
PUM
Login to View More
Abstract
The invention discloses an online calculation method of probability power flow based on BP neural network, which mainly includes the following steps: 1) establishing a BP neural network power flow model. 2) Initialize the basic parameters of the BP neural network power flow model. 3) Obtain training sample data. 4) Determine the training target. The training sample data is used to train the BP neural network power flow model, so as to obtain the trained BP neural network power flow model. 5) Obtain calculation samples. 6) Input the training sample data obtained in step 3 into the BP neural network power flow model trained in step 4 at one time to obtain the training target, thereby judging the solvability of the power flow of all training samples; calculating the power flow value of the solvable sample . 7) Statistical probability power flow index. The invention can be widely applied to the online calculation of the probability flow of the electric powersystem, and is especially suitable for the situation that the high proportion of new energy access causes the uncertainty of the electric powersystem to increase.
Description
technical field [0001] The invention relates to the field of electric powersystem 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
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More
Application Information
Patent Timeline
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to View More 
Login to View More 
