Date of Award

4-2012

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

First Advisor

Dr. Johnson A. Asumadu

Second Advisor

Dr. Ala Al-Fuqaha

Third Advisor

Dr. Liang Dong

Abstract

With the proliferation of nonlinear loads in the power system, harmonic pollution becomes a serious problem that affects the power quality in both transmission and distribution systems. Active power filters (APF) have been proven to be one of the most successful methods for mitigating harmonics problems. So far, different techniques have been used in harmonics extraction and control of APF to satisfy the fast response and the accuracy required by the APF. Neural networks techniques have been used successfully in different real-time and complex situations. This dissertation demonstrates four main tasks; (i) a novel adaptive radial basis function neural networks (RBFNN) algorithm. This algorithm can be used in different signal processing or control applications,(ii) dynamic identification for the total harmonics content in converter waveforms based on RBFNN and p-q (real powerimaginary power) theory, (iii) RBFNN is used to dynamically identify and estimate selective harmonic components in converter waveforms, and (iv) a novel adaptive hysteresis current control algorithm with nearly constant switching frequency. The proposed RBFNN filtering algorithms are based on a computationally efficient training method called hybrid learning method, which requires negligible training time. Both of the proposed algorithms in this dissertation, adaptive RBFNN algorithm and adaptive hysteresis current controller, are simple, effective, and easy to implement.

Access Setting

Dissertation-Open Access

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