Applications of fuzzy logic and neural networks in power electronics and power systems

Abstract

There are typically three frameworks with some generalization of functionalities, i.e., three paradigms, that can be used for energy conversion systems, with artificial-intelligence-based computation: a function approximation or input/ output mapping, a negative feedback control, and a system optimization. The first one is the construction of a model, using either heuristic or numerical data, the second one is the comparison of a set point with an output that can be either measured or estimated with a function that minimizes the error of the set point with the output, and the third one is a search for parameters and system conditions that will maximize or minimize a given function. Fuzzy logic and neural network techniques make the implementation of such three paradigms possible, robust, and reliable in practical cases. The integration of modern power electronics, power systems, communications, information, and cyber technologies with a high penetration of renewable energy resources has been at the edge and at the frontier for the design and implementation of smart-grid technology. The emergence of AI techniques in past industrial applications is allowing smart-grid technology to be an interdisciplinary field with multiple dimensions of complexity. This chapter will present some background and established applications of AI in power electronics, power systems, and renewable energy systems.