Author ORCID Identifier
Year of Publication
Doctor of Philosophy (PhD)
Electrical Engineering and Computer Science
Dr. Yuan Liao
Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the autocorrelation of the dataset being predicted. The performance of MLPs will be presented in terms of root-mean-square error (RMSE) between actual and predicted voltage magnitude and voltage phase angles and training execution time for distribution system state estimation (DSSE). The performance of CNNs, and LSTMs will be presented in terms of RMSE between actual and predicted real power demand and execution time when performing distribution system state forecasting (DSSF). Additionally, Bayesian Optimization with Gaussian Processes are used to optimize MLPs for regression. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting respectively.
Digital Object Identifier (DOI)
Carmichael, James Paul, "Application of Conventional Feedforward and Deep Neural Networks to Power Distribution System State Estimation and State Forecasting" (2023). Theses and Dissertations--Electrical and Computer Engineering. 189.