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MS Final Examination – Kelly Tray


Tuesday, June 6, 2017 2:00 PM - 4:00 PM

Dynamic Composite Load Signature Detection and Classification using Supervised Learning over Disturbance Data
Load modeling that can accurately represent the dynamics behavior of the generation and loads is really important in operation and planning studies for the transmission and distribution systems. Yet, it is a complex subject in power system research communities and utilities. The composition of the end-use loads is changing continually based on climate zone, season, and time. The WECC composite load model has been developed recently to better represent Fault Induced Delayed Voltage Recovery (FIDVR) events, which are caused by air-conditioning stalling phenomena. The approach is based on using the information of the load class at the substation level and the composition of air-conditioning, induction machines, and power electronics associated with the load class. Therefore, it is important to be able to identify and classify the load class. This can be accomplished by using machine learning based signature detection since each load class has unique signature response due to a particular disturbance in the system.

The objective of this project is to implement a supervised learning, Artificial Neural Network (ANN) algorithm, to detect and classify the composite load signatures in terms of residential, commercial, agriculture, and mixed class. Furthermore, the process of creating WECC composite load model data, using Load Model Data Tool (LMDT), to be used in time-domain dynamic simulation (PSS/E) is demonstrated. The 24-Bus Reliability Test System will be used for the purpose of demonstration and validation of our proposed methodologies.

Major Advisor: Ted Brekken
Committee: Eduardo Cotilla-Sanchez
Committee: Julia Zhang
GCR: Yun-Shik Lee


Kelley Engineering Center (campus map)
1005
Calvin Hughes
1 541 737 3168
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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