Method for identifying a fault event in an electric power distribution grid sector

Patent metadata

Field	Value
Publication number	US-2018316176-A1
Application number	US-201815961180-A
Country	US
Kind code	A1
Filing date	Apr 24, 2018
Priority date	Apr 26, 2017
Publication date	Nov 1, 2018
Grant date	—

Abstract

A method for identifying a fault event in an electric power distribution grid sector including one or more electric loads and having a coupling node with a main grid, at which a grid current adsorbed by said electric loads is detectable. The method allows determining whether a detected anomalous variation of the grid current, adsorbed at the electric coupling node, is due to the start of a characteristic transitional operating period of an electric load or is due to an electric fault.

First claim

1 . A method for identifying a fault event in an electric power distribution grid sector, said grid sector including one or more electric loads (L 1 , . . . , L M ) and having a coupling node (PoC) with a main grid, at which a grid current (I G ) of said grid sector is detectable, said method comprising:
a) acquiring, for each electric phase, first data values (i k [n]) indicative of said grid current (I G ), said first data values being acquired at subsequent sampling instants (n) subdivided in a sequence of time windows (TW 1 , . . . , TW R ); b) processing first data values (i k + [n]) acquired, for each electric phase, at first sampling instants (n) at least partially included in a time window (TW + ) and first data values (i k − [n]) acquired, for each electric phase, at a second sampling instants (n) preceding said first sampling instants and at least partially included in a previous time window (TW − ) preceding said time window (TW + ) to check whether said grid current (I G ), at said time window (TW + ), is subject to an anomalous variation with respect to said previous time window (TW − ); c) if it is determined that said grid current (I G ) is not subject to an anomalous variation with respect to said previous time window (TW − ), repeating said step (b) for subsequent sampling instants; d) if it is determined that said grid current (I G ), starting from an event instant (n event ) of said time window (TW + ), is subject to an anomalous variation (ΔI G ) with respect to said previous time window (TW − ), processing one or more first data values (i k e [n]) acquired, for each electric phase, at sampling instants following said event instant (n event ) to calculate, for each electric phase, second data values (i k clean [n]) indicative of the anomalous variation (ΔI G ) of said grid current (I G ); e) processing said second data values (i k clean [n]) calculated for each electric phase to check whether the anomalous variation of said grid current (I G ) is due to a characteristic transitional operating period of an electric load of said grid sector.

2 . The method, according to claim 1 , wherein said step b) further comprises the following:
for each electric phase (k) of said grid sector, executing the following steps:
selecting a first vector (i k + [n]) of first data values (i k (n)) acquired at said first sampling instants (n);
selecting a second vector (i k − [n]) of first data values (i k (n)) acquired at said second sampling instants (n);
processing said first and second vectors (i k + [n]), (i k − [n]) to calculate a phase current variation value (CH k [n]) indicative of a variation in a phase current of said grid current (I G ) with respect to said previous time window (TW − );
processing the phase current variation values (CH k [n]) calculated for each electric phase to calculate an overall current variation value (CH[n]) indicative of an overall variation of said grid current (I G ) with respect to said previous time window (TW − ); comparing said overall current variation value (CH[n]) with a first threshold value (TH 1 ); repeating the previous steps for a first number (N1) of sampling instants (n) included in said time window (TW+); checking whether said overall current variation value (CH[n]) exceeds said first threshold value (TH 1 ) for said first predefined number (N1) of sampling instants (n).

3 . The method, according to claim 1 , wherein said step d) comprises the following:
for each electric phase,
selecting a first data set (i k e [n]) of first data values (i k (n)) acquired at sampling instants following said event instant (n event );
selecting a second data set (i k r [n]) of first reference data values indicative of a background condition of said grid current (I G );
processing said first and second data sets (i k e [n]), (i k r [n]) of data values to calculate a third data set (i k clean [n]) of said second data values.

4 . The method, according to claim 3 , wherein said reference data values (i k r [n]) are first data values (i k (n)) acquired at one or more sampling instants (n) preceding said event instant (n event ).

5 . The method, according to claim 4 , wherein said reference data values (i k r [n]) are first data values (i k − [n]) acquired at the last time window (TW − ) preceding said event instant (n event ).

6 . The method, according to claim 1 , wherein said step e) further comprises the following:
processing said second data values (i k clean [n]) calculated for each electric phase to calculate third data values (I clean [n]) indicative of the anomalous variation (ΔI G ) of said grid current (I G ); for each electric load (L 1 , . . . , L M ), selecting second reference data values (I m [n]) indicative of a predicted current absorbed by said electric load during a characteristic transitional operating period of said electric load; for each electric load (L 1 , . . . , L M ), processing said third data values (I clean [n]) and said second reference data values (I m [n]) to calculate an error value (E m [n]) indicative of a difference between the anomalous variation (ΔI G ) of said grid current (I G ) and the predicted current absorbed by said electric load during said characteristic transitional operating period; selecting a minimum error value (E*[n]) among the error values (E m [n]) calculated for said electric loads (L 1 , . . . , L M ); comparing said minimum error value (E*[n]) with a second threshold value (TH 2 ); repeating the previous steps for a second number (N2) of sampling instants (n) following said event instant (n event ); checking whether said minimum error value (E*[n]) exceeds said second threshold value (TH 2 ) for said second number (N2) of sampling instants.

7 . The method, according to claim 1 , wherein said one or more second reference data values (I m [n]) are calculated by simulating the behaviour of each electric load (L 1 , . . . , L M ) using a time-discrete model (Y( )) describing the operation of said electric load during said characteristic transitional operating period.

8 . The method, according to claim 7 , wherein said time-discrete model (Y( )) is calculated by performing a modelling procedure that comprises the following steps:
activating an electric load (L m ) of said grid sector; deactivating the remaining electric loads of said grid sector; acquiring detection data indicative of the operating voltage and of the current of said electric load during said characteristic transitional operating period of said electric load; processing said detection data to estimate one or more actual electrical and/or mechanical parameters (p est ) of said electric load to be used in said time-discrete model (Y( )).

9 . The method, according to claim 8 , wherein said actual electrical and mechanical parameters (p est ) of said electric load (L m ) are estimated by solving a NLS problem based on one or more installation constraints provided for said electric load.

10 . The method, according to claim 1 , wherein said electric loads (L 1 , . . . , L M ) are formed by electric rotating machines or groups of electric rotating machines, the characteristic transitional operating period of said electric loads being a start-up phase of said electric rotating machines or groups of electric rotating machines.

11 . A computer storage medium comprising:
a set of instructions structured to be executed by a processor effective to: a) acquire, for each electric phase, first data values (i k [n]) indicative of a grid current (I G ), said first data values being acquired at subsequent sampling instants (n) subdivided in a sequence of time windows (TW 1 , . . . , TW R ); b)

Assignees

• Abb Spa

Inventors

• Ragaini Enrico
• Angelosante Daniele
• Fagiano Lorenzo

Classifications

• G06Q10/0639 Primary

  Performance analysis of employees; Performance analysis of enterprise or organisation operations · CPC title

• H02H3/083

  for three-phase systems · CPC title

• H02H1/0092 Primary

  concerning the data processing means, e.g. expert systems, neural networks · CPC title

• H02H7/26

  Sectionalised protection of cable or line systems, e.g. for disconnecting a section on which a short-circuit, earth fault, or arc discharge has occured (locating faults in cables G01R31/08) · CPC title

• G01R19/16528

  using digital techniques or performing arithmetic operations (using digital techniques to measure a voltage or a current, see G01R19/25) · CPC title