New approaches to the AGC non-conforming load problem

Author

Douglas, L.D. ; Green, T.A. ; Kramer, R.A.

Author_Institution

Johnson Yokogawa Corp., Carlton, TX, USA

Volume

9

Issue

2

fYear

1994

fDate

5/1/1994 12:00:00 AM

Firstpage

619

Lastpage

628

Abstract

Northern Indiana Public Service Company (NIPSCO) has many nonconforming loads that are a challenge for their current automatic generation control (AGC) software. This paper presents two techniques that address the nonconforming load problem for AGC. One technique used a neural network algorithm for pattern recognition of controllable signals, and the other technique is based on the detection of a controllable signal in the presence of a noisy random load using a random signal probability model. Both algorithms were tested with actual field load data via a dispatcher training simulator that utilized a generic system model

Keywords

control system analysis computing; digital simulation; load dispatching; neural nets; power system analysis computing; power system computer control; USA; automatic generation control; computer simulation; controllable signals; generic system model; load dispatcher; neural network algorithm; noisy random load; nonconforming loads; pattern recognition; power systems; random signal probability model; software; Automatic control; Automatic generation control; Error correction; Frequency; Furnaces; Iron; Milling machines; Power industry; Production; Steel;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.317682

Filename

317682