Unlock instant, AI-driven research and patent intelligence for your innovation.
Power grid parameter identification method based on particle swarm optimization algorithm and local state estimation
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 technology of particle swarm optimization and state estimation, which is applied in the field of identification and estimation of basic parameters of the power grid, can solve problems such as multi-correlated suspicious parameter problems that cannot be solved, and achieve the effect of ensuring accuracy and improving estimation accuracy
Active Publication Date: 2017-01-04
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
View PDF5 Cites 2 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
However, this method can only solve the problem of single suspicious parameter estimation, and cannot solve the problem of multi-correlated suspicious parameters.
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.
[0033] Set the branch parameters R, X, and B of branch 103 (the first node is 66, and the last node is 67) as error values (per unit value), change R from 0.0224 to 0.0884, X from 0.1015 to 0.2025, and B from 0.02682 was changed to 0.08682.
[0034] Step 1: Determination of local partitions of suspicious parameters
[0035] From the perspective of the physical topology model, use the breadth-first search method to search for branches directly connected to the branch 103, U 103 = {98, 99, 100, 101, 102}, such as figure 2 shown;
[0036] Step 2: Suspicious parameter local partition state estimation
[0037] Obtain the branch measurement of branches 98, 99, 100, 101, 102, 103 and the node injection power measurement and voltage measurement of nodes 66 and 67 to meet the observable and estimable requirements of the state estimation. State estimation calculation;
[0038] Step 3: Determination of evaluation indicators for suspic...
[0060] Artificially set the branch parameters R, X, and B of branch 30 (the first node is 23, and the last node is 24) to be wrong values (per unit values), change R from 0.0135 to 0.0835, X from 0.0492 to 0.1492, and B From 0.0498 to 0.0898;
[0061] Set the branch parameters R, X, and B of branch 31 (the first node is 23, and the last node is 25) as wrong values (per unit value), change R from 0.0156 to 0.0956, X from 0.08 to 0.18, and B from 0.0864 changed to 0.1864;
[0062] Set the branch parameters K and X of branch 32 (the first node is 25, and the last node is 26) as error values (per unit value), change K from 0.960 to 0.920, and X from 0.0382 to 0.1382.
[0063] Taking the branch 30 as an example, the parameter adjustment method is described below:
[0064] Step 1: Determination of local partitions of suspicious parameters
[0065] Step A1: Considering the physical topology model, use the breadth-f...
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 belongs to the field of power grid basic parameter identification and estimation and particularly relates to a power grid parameter identification method based on a particle swarm optimizationalgorithm and local state estimation. Based on the characteristic that errors of different parameters all lead to increases in a state estimation target function value, the method utilizes a particle swarm optimizationalgorithm to optimize the adjustment of a suspicious parameter and ultimately determines a suspicious parameter adjustment corresponding to a minimal assessment index, thereby achieving suspicious parameter adjustment. The method is used to adjust a suspicious parameter appearing during power grid operation, and provides reliable raw data for executing various EMS application module functions. The method employs the modern optimization algorithm to correct suspicious element parameters generated by offsets or errors of a power grid model in a real power grid, ensures the accuracy of basic parameters of power grid operation, and improves the practical level of various high-level analysis and applications and the accuracy, reliability and precision of power scheduling control of a power scheduling control center EMS.
Description
technical field [0001] The invention belongs to the field of electric network basic parameter identification and estimation, and in particular relates to an electric network parameter identification method based on particle swarm optimization algorithm and local state estimation. Background technique [0002] At present, one of the development trends of the power system is grid interconnection, which objectively requires each control center (system) related to the grid to establish a unified online computing model for the entire network. In actual operation, various professional functional departments such as dispatch center automation, operation mode and relay protection have established independent model parameter libraries and maintained them separately due to business needs, resulting in the situation of "one power grid, multiple sets of parameters". This results in poor consistency and accuracy of parameters across functions. In addition, due to the large scale and rap...
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