Neural Networks for Estimating and Compensating the Nonlinear Characteristics of Nonstationary Complex Systems

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NEURAL NETWORKS FOR ESTIMATING AND COMPENSATING THE NONLINEAR CHARACTERISTICS OF NONSTATIONARY COMPLEX SYSTEMSABSTRACTScope and Intellectual Merit. The objective of this research is to find a method of accurately quantifying the distorted currents and voltages created by certain devices in power networks. Distortion causes electromagnetic inference with communication and the fast growing digital world, light flicker, overheating of electric machines and transformers and increased losses in transmission lines. For years utilities and customers have argued about who causes the distortion. Existing measurement techniques can lead to errors of up to 40%. The approach is to use Echo State Networks and Simultaneous Recurrent Neural Networks with super fast learning algorithms (biological inspired algorithms such as particle swarm optimization), and other computational intelligence algorithms, to accurately measure the distortion by monitoring only voltage and current without the need for added transducers. Such fast and powerful neural networks could also be used for closed loop control of the offending nonlinear devices to mitigate the distortion.Broader Benefits. The economic impact of applying brain-like techniques to monitor and control physical processes is significant. Reduced power losses mean savings and more useful power over the same lines. More secure and reliable power systems of high quality are of national interest. Moreover, reduced electromagnetic interference promotes a cleaner more reliable telecommunications and digital environment. Fast intelligent nonlinear controllers will also benefit other real-world high-speed closed loop controlled nonlinear non-stationary processes. There exists a talent shortage in the US in the application of intelligent systems, and the project will train a new generation of professionals, and educators, underrepresented minorities and undergraduates in the multiple fields of the project