Term of Award

Spring 2015

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Electrical Engineering

Committee Chair

Rami Haddad

Committee Member 1

Youakim Kalaani

Committee Member 2

Frank Goforth

Committee Member 3

Adel El Shahat

Committee Member 3 Email

aahmed@georgiasouthern.edu

Abstract

Distributed Generation (DG) sources have become an integral part of modern decentralized power systems. However, the interconnection of DG systems to the power grid can present several operational challenges. One such major challenge is islanding detection. Islanding occurs when a DG system is disconnected from the rest of the power grid. Islanding can present serious safety hazards and therefore an accurate and fast islanding detection technique is mandated by DG interconnection standards such as IEEE 1547 and UL 1741. Conventional islanding detection techniques passively monitor the local power system parameters such as voltage and frequency to detect islanding. These techniques have large non-detection zones and are prone to nuisance tripping. Therefore, two improved and computationally inexpensive passive islanding detection techniques for inverter-based DG systems were proposed. The techniques monitor the ripple content in the rate of change of frequency and voltage amplitude waveforms using time domain-spectral analysis. The proposed techniques were tested for inverter-based DG systems modeled according to IEEE 929-2000 standard. Results indicated that both techniques were not only capable of detecting islanding, but also able to accurately distinguish between islanding and non-islanding events under a wide range of operating conditions. Furthermore, a novel Smart DG system which is able to detect and classify events was proposed. This added intelligence has considerable impact on the safety and operation of such DG systems. This feature will help the system operator develop a clear understanding of the operating requirements needed to mitigate the effects of such events. The event classification technique has been implemented using artificial neural networks (ANN) with a set of local input parameters. Five parallel ANNs have been designed with a majority vote final stage to represent the final classification output. A total of 310 event cases have been generated to test the performance of the technique. This technique classified the events within 10 cycles of their occurrence with a 98% average classification accuracy.

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