|dc.description.abstract||Today’s Electricity market is highly interconnected. The actions of customers,
transmission and distribution systems, generation systems, markets, and service
providers are correlated. The dynamic stability of the grid depends on the dynamic
characteristics of all these components and systems. The least understood component is the consumer load. While average power is metered for billing, the voltage
or current relationships and dynamic characteristics of loads are not monitored.
It is the purpose of this work to analyze high-bandwidth voltage and current measurements from an office building to characterize certain load features. The focus
is on transient events under the premise that some features will be revealed when
loads switch on and off, or change mode of operation. This approach involves certain
tasks. First an event needs to be identified, and the duration of transient needs to
be estimated. Second, features that might be useful for classifying transients must
be determined. Third, classification is performed using machine learning Techniques.
Signal Processing tools based on changes in current waveforms are used to detect
events. Many features are examined in this work including voltage and current
harmonics, power factor, active, reactive, and distortion power. Machine Learning
techniques guide the classification task. These include self-organising maps and neural network.
Analysis and results are presented based on actual load measurements.||en_US