Online Design of an Echo State Network Based Wide Area Monitor for a Multimachine Power System
With deregulation and growth of the power industry, many power system elements such as generators, transmission lines, are driven to operate near their maximum capacity, especially those serving heavy load centres. Wide Area Controllers (WACs) using wide area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc. To damp out system oscillations. However, since the power system is highly nonlinear with fast changing dynamics, it is a challenging problem to design an online system monitor/estimator, which can provide dynamic intra-area and inter-area information such speed deviations of generators to an adaptive WAC continuously. This paper presents a new kind of recurrent neural networks, called the Echo State Network (ESN), for the online design of a Wide Area Monitor (WAM) for a multimachine power system. A single ESN is used to predict the speed deviations of four generators in two different areas. The performance of this ESN WAM is evaluated for small and large disturbances on the power system. Results for an ESN based WAM and a Time-Delayed Neural Network (TDNN)-based WAM are presented and compared. The advantages of the ESN WAM are that it learns the dynamics of the power system in a shorter training time with a higher accuracy and with considerably fewer weights to be adapted compared to the design-based on a TDNN.
G. K. Venayagamoorthy, "Online Design of an Echo State Network Based Wide Area Monitor for a Multimachine Power System," Neural Networks, Elsevier, Jan 2007.
The definitive version is available at https://doi.org/10.1016/j.neunet.2007.04.021
Electrical and Computer Engineering
National Science Foundation (U.S.)
Keywords and Phrases
Echo State Network; Multimachine Power System; Online Training; Time-Delayed Neural Network; Wide Area Control System; Wide Area Monitor; System identification
International Standard Serial Number (ISSN)
Article - Journal
© 2007 Elsevier, All rights reserved.
01 Jan 2007