Abstract
This chapter extends the applicability of the presented intelligent fault diagnosis (IFD) scheme combining the advanced signal processing and machine learning tools in Chapter 10 on the IEEE 13-node test distribution feeder under load and renewable energy generation uncertainty. It shows the step-by-step modeling of the selected test feeder incorporated with intermittent renewable energy resources in RSCAD software and simulation of the modeled feeder in the real-time digital simulator rack. This chapter also presents the load demand and renewable energy generation uncertainty modeling using the appropriate probability density function. Then, it illustrates the faulty data generation and recording processes using phasor measurement units (PMUs). Besides, it presents the obtained results using the IFD scheme based on advanced signal processing and machine learning tools. Moreover, this chapter investigates the IFD scheme efficacy in the presence of measurement noise and under contingencies, for example, branch and distributed generator (DG) outages. Finally, it presents the IFD scheme validation results obtained from the RSCAD recorded and physical PMU retrieved data.
Published Version
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