This dissertation proposes to develop, leverage, and apply machine learning algorithms on various smart grid applications including state estimation, false data injection attack detection, and reliability evaluation. The dissertation is divided into four parts as follows.. Part …
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We present an application of a robust and fast parallel algorithm to power system state estimation with minimal amount of modifications to existing state estimators presently in place using the Auxiliary Problem Principle. We demonstrate its effectiveness on IEEE test systems, t…
The evolving complexity of electric power systems with higher levels of uncertainties is a new challenge faced by system operators. Therefore, new methods for power system prediction, monitoring and state estimation are relevant for the efficient exploitation of renewable energy…
In the United States, landfilling remains the primary choice for the disposal of municipal solid waste (MSW). The MSW in landfills undergoes anaerobic decomposition by various micro-organisms and produces large amounts of landfill gas (LFG). LFG, a gaseous mixture consisting mai…
Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. With the development of Smart Grid, false data injection attack has recently attracted wide research interest. This thesis proposes a false data attack model with inco…
A deep neural network is a deep learning algorithm that uses artificial neural networks with multiple layers. The goal of a deep neural network is to learn a function from observation samples. In many real-world problems, this function is unknown and there is a need to learn it …