公开(公告)号：US20170074250A1

公开(公告)日：2017-03-16

申请号：US15359976

申请日：2016-11-23

Applicant: ABB Schweiz AG

Inventor： Rongrong Yu ,  Niya Chen ,  Yao Chen

IPC: F03D17/00 ,  G06N99/00

CPC classification number: F03D17/00 ,  F05B2260/80 ,  F05B2260/83 ,  F05B2260/84 ,  G06N99/005

Abstract: The present application includes wind turbine condition monitoring method and system. The method comprises: acquiring historical SCADA data, and wind turbine reports corresponding to the historical SCADA data; training an overall model for overall diagnosing the wind turbine, and training different individual models for analyzing different components of the wind turbine based on the historical SCADA data and the corresponding wind turbine report, by establishing relationship between the historical SCADA data and the wind turbine report; acquiring real time SCADA data, inputting the real time SCADA data to the trained overall model, obtaining the health condition of the wind turbine from the trained overall model, and performing individual diagnosing step if the trained overall model determines wind turbine as defective status; inputting the real time SCADA data to the trained individual model corresponding to the defective component, and obtaining the fault details of the defective component from the trained individual model corresponding to the defective component.

Abstract translation: 本申请包括风力发电机状况监测方法和系统。 该方法包括：获取与历史SCADA数据相对应的历史SCADA数据和风力发电机报告; 培训风力发电机整体诊断总体模型，并根据历史SCADA数据和相应的风力发电机报告，通过建立历史SCADA数据与风力发电机报告之间的关系，对不同的风力发电机组件进行不同的模型分析 ; 获取实时SCADA数据，将实时SCADA数据输入到经过训练的总体模型，从训练有素的整体模型中获取风力涡轮机的健康状况，并且如果训练有素的整体模型将风力涡轮机确定为缺陷状态，则执行个体诊断步骤; 将实时SCADA数据输入到与故障部件相对应的经过训练的单独模型，并且从与缺陷部件对应的经过训练的单独模型中获得缺陷部件的故障细节。

公开(公告)号：US11300603B2

公开(公告)日：2022-04-12

申请号：US17178521

申请日：2021-02-18

Applicant: ABB Schweiz AG

Inventor： Jiayang Ruan ,  Niya Chen ,  Rongrong Yu ,  Kai Hencken ,  Joerg Lehmann

IPC: G01R31/12

Abstract: A method and system for condition monitoring electrical equipment includes detecting partial discharge impulses generated in the electrical equipment for at least one power cycle; obtaining first data representing the detected partial discharge impulses for the at least one power cycle; obtaining second data by phase-resolving the first data; obtaining third data by regularizing the second data; analyzing the third data into a first component and at least one second component; obtaining phase information of the first component; shifting the first component and the at least one second component in consideration of the obtained phase information; and generating a signal indicating a health state of the electrical equipment by comparing the shifted first and second components of the third data with their counterparts associated with an unhealthy electrical equipment.