Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Markovian architectural bias of recurrent neural networks
IEEE Transactions on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A PSO with quantum infusion algorithm for training simultaneous recurrent neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Particle swarm optimization with quantum infusion for system identification
Engineering Applications of Artificial Intelligence
What makes a brain smart? reservoir computing as an approach for general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
A method for online analysis of structured processes using bayesian filters and echo state networks
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
International Journal of Wireless and Mobile Computing
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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.