公开(公告)号：WO2019137916A1

公开(公告)日：2019-07-18

申请号：PCT/EP2019/050345

申请日：2019-01-08

Applicant: ABB SCHWEIZ AG

Inventor： GREYER, Tobias ,  SPUDIC, Vedrana ,  GUTSCHER, Christof ,  RUEETSCHI, Andrea

IPC: H02P27/12 ,  H02P21/30 ,  H02P27/14 ,  H02M7/487 ,  H02M7/5395 ,  H02M7/5387

CPC classification number: H02P27/12 ,  H02M7/487 ,  H02M7/5395 ,  H02M2007/53876 ,  H02P21/30 ,  H02P27/14

Abstract: A method for controlling an electrical converter (12), the electrical converter (12) being adapted for converting a DC voltage (v dc ) into a multi-phase voltage with at least two voltage levels, comprises: determining a modulating signal vector (u* abc ) from a stator flux reference vector (ψ* s, αβ ); determining a switching pattern (54) from the modulating signal vector (u* abc ) via pulse width modulation, the switching pattern (54) comprising a sequence of switching transitions, wherein a switching transitions defines a switch position, at which a phase of the converter is switched from one voltage level to another voltage level, and a transition time instant at which the phase of the converter is switched; determining a stator flux error ( ψ* s,err,αβ ) by subtracting the stator flux reference vector (ψ* s,αβ ) from an estimated stator flux vector (ψ s,αβ ), which is estimated from measurements in the electrical converter (12); modifying the switching pattern (54) by moving transition time instants of switching transitions of the switching pattern (54), such that the stator flux error (ψ s,err,αβ ) is minimized; and applying at least a part of the modified switching pattern to the electrical converter (12).

公开(公告)号：EP4227744A1

公开(公告)日：2023-08-16

申请号：EP22156590.6

申请日：2022-02-14

Applicant: ABB Schweiz AG

Inventor： SIVANTHI, Thanikesavan ,  RUEETSCHI, Andrea

IPC: G05B9/02 ,  G05B19/414 ,  G05B19/042

Abstract: A control arrangement (22) for controlling an electronic device (26), is provided. The control arrangement (22) comprises: an inner controller (30) being coupled to the electronic device (26) and being configured for generating at least one control signal for controlling the electronic device (26) depending on at least one instruction signal from an outer controller (50); and a smart controller (32) coupled to the inner controller (30) and being configured for providing at least one substitute instruction signal for controlling the electronic device (26) to the inner controller (30) if an abnormality related to a signal traffic between the inner controller (30) and the outer controller (50) is detected.

公开(公告)号：EP3729637A1

公开(公告)日：2020-10-28

申请号：EP19700121.7

申请日：2019-01-08

Applicant: ABB Schweiz AG

Inventor： GEYER, Tobias ,  SPUDIC, Vedrana ,  GUTSCHER, Christof ,  RUEETSCHI, Andrea

IPC: H02P27/12 ,  H02P21/30 ,  H02P27/14 ,  H02M7/487 ,  H02M7/5395 ,  H02M7/5387

公开(公告)号：EP4231154A1

公开(公告)日：2023-08-23

申请号：EP22156985.8

申请日：2022-02-16

Applicant: ABB SCHWEIZ AG

Inventor： SIVANTHI, Thanikesavan ,  RUEETSCHI, Andrea ,  SCHEUSS, Oliver ,  HUANG, Pengcheng ,  GIANNOPOULOU, Georgia

IPC: G06F11/34 ,  G06F30/27 ,  G06F11/36 ,  G06N3/08

Abstract: A method for providing a training dataset for training and/or testing a machine learning model (20) for predicting timing and/or resource requirements of an unknown application (52) running on an electronic device is provided. The method comprising: providing a training module configured for showing a behaviour as a predetermined electronic device; obtaining a plurality of representative applications (26), which are known and configured to run on the electronic device; running the plurality of representative applications (26) on the training module; recording at least one value of at least one performance metric for each of the representative applications (26) when running on the training module; extracting at least one feature from the representative applications (26); and associating the extracted features, the corresponding recorded values of the performance metric and the corresponding performance metrics in the training dataset; and providing the dataset for training and/or testing the machine learning model (20).