Work

Application of neural networks to load-frequency control in power systems

Year: 1994

Type: article

Abstract: This paper describes an application of layered neural networks to nonlinear power systems control. A single generator unit feeds a power line to various users whose power demand can vary over time. As a consequence of load variations, the frequency of the generator changes over time. A feedforward neural network is trained to control the steam admission valve of the turbine that drives the generator, thereby restoring the frequency to its nominal value. Frequency transients are minimized and zero steady-state error is obtained. The same technique is then applied to control a system composed of two single units tied together through a power line. Electric load variations can happen independently in both units. Both neural controllers are trained with the back propagation-through-time algorithm. Use of a neural network to model the dynamic system is avoided by introducing the Jacobian matrices of the system in the back propagation chain used in controller training.

Source: Neural Networks

DOI: 10.1016/0893-6080(94)90067-1

Authors: Françoise Beaufays, Youssef Abdel-Magid, Bernard Widrow

Institutions: Stanford University, King Fahd University of Petroleum and Minerals

Language: English

Topic: Frequency Control in Power Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,110