公开(公告)号：US10935581B2

公开(公告)日：2021-03-02

申请号：US16120907

申请日：2018-09-04

Applicant: Northeastern University

Inventor： Huaguang Zhang ,  Jun Yang ,  Jie Bai ,  Gang Wang ,  Qiuye Sun ,  Xinrui Liu ,  Yingchun Wang ,  Dongsheng Yang ,  Zhiliang Wang ,  Bonan Huang ,  Zhanshan Wang ,  Yanhong Luo

IPC: G01R21/133

Abstract: The invention provides an electric grid state estimation system and method based on a boundary fusion. The system includes an electric grid data acquisition module, a communication module including a local data unit and a state estimation unit, and a data fusion module, wherein the state estimation unit includes a memory storing a state estimation program and a display displaying a program running and outputting a state variable; the state estimation program is performed to realize an electric grid state estimation; the estimation method includes the following steps of dividing a regional electric grid, then establishing a measurement equation for each region, solving an internal quantity and a boundary quantity, fusing the boundary quantities of two regions, correcting the boundary quantity, performing a non-linear transformation on the intermediate variable, solving the estimated values of the state variable by the least square method, and performing outputting.

公开(公告)号：US09797799B2

公开(公告)日：2017-10-24

申请号：US14692502

申请日：2015-04-21

Applicant: Northeastern University

Inventor： Huaguang Zhang ,  Dazhong Ma ,  Jian Feng ,  Jinhai Liu ,  Gang Wang ,  Zhenning Wu ,  Qiuye Sun ,  Xiaoyu Li

IPC: G01M3/28 ,  G01M3/24

CPC classification number: G01M3/2807 ,  G01M3/243

Abstract: The present invention relates to an intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data. The present invention effectively analyzes a large amount of data collected on site within a reasonable time period and obtains a state of a pipeline network by an intelligent adaptive method, thereby obtaining a topological structure of a pipeline network. The present invention specifically adopts a flow balance method in combination with information conformance theory to analyze whether the pipeline network has leakage; small amount of leakage and slow leakage can be perfectly and accurately alarmed upon detection; as a generalized regression neural network is adopted to locate a leakage of the pipeline network, an accuracy of a result is increased. Therefore, the present invention adopts a policy and intelligent adaptive method based on big data to solve problems of detecting and locating leakage of the pipeline network.

公开(公告)号：US11488010B2

公开(公告)日：2022-11-01

申请号：US16345657

申请日：2019-02-13

Applicant: Northeastern University

Inventor： Jin hai Liu ,  Ming rui Fu ,  Sen xiang Lu ,  Hua guang Zhang ,  Da zhong Ma ,  Gang Wang ,  Jian Feng ,  Xin bo Zhang ,  Ge Yu ,  Hong qiu Wei

IPC: G06N3/08 ,  G06N3/04 ,  G01N27/82 ,  G06Q10/00 ,  G06N5/00 ,  G06N20/20

Abstract: Provided is an intelligent analysis system for inner detecting magnetic flux leakage (MFL) data in pipelines, including a complete data set building module, a discovery module, a quantization module and a solution module, wherein: a complete data set building method is adopted in the complete data set building module to obtain a complete magnetic flux leakage data set; a pipeline connecting component discovery method is adopted in the discovery module to obtain the precise position of a weld; an anomaly candidate region search and identification method is adopted in the discovery model to find out magnetic flux leakage signals with defects; a defect quantization method based on a random forest is adopted in the quantization module to obtain a defect size; and a pipeline solution based on an improved ASME B31G standard is adopted in the solution module to output an evaluation result.