Date of Award


Document Type


Degree Name



Computer Science and Engineering

First Advisor

Wenzhong Gao

Second Advisor

Jing Li

Third Advisor

Amin Khodaei

Fourth Advisor

Mohammad A. Matin


Artificial intelligence, Machine learning


With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control.

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data hidden information. The data is transmitted with an Open System Interconnection (OSI) model to the data visualization platform with a high-speed communication architecture. Google Earth and Global Geographic Information System (GIS) are used to design the visualization platform and realize the results.

Based on the data visualization platform above, the external manifestation of the data is studied. In the following work, I try to understand the internal hidden information of the data. A short-term load forecasting approach is designed based on support vector regression (SVR) to provide a higher accuracy load forecasting for the network reconfiguration. The nonconvexity of three-phase balanced optimal power flow is relaxed to an optimal power flow (OPF) problem with the second-order cone program (SOCP). The alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the reality of distribution systems, a three-phase unbalanced distribtion system is built, which consists of the hourly operation scheduling at substation level and the minutes power flow operation at feeder level. The operaion cost of system with renewable generation is minimized at substation level. The stochastoc distribution model of renewable generation is simulated with a chance constraint, and the derived deterministic form is modeled with Gaussian Mixture Model (GMM) with genetic algorithm-based expectationmaximization (GAEM). The system cost is further reduced with OPF in real-time (RT) scheduling. The semidefinite programming (SDP) is used to relax the nonconvexity of the three-phase unbalanced distribution system into a convex problem, which helps to achieve the global optimal result. In the parallel manner, the ADMM is realizing getting the results in a short time.

Clouds have a big impact on solar energy forecasting. Firstly, a convolutional neural network based mathod is used to estimate the solar irradiance, Secondly, the regression results are collected to predict the renewable generation. After that, a novel approach is proposed to capture the Global horizontal irradiance (GHI) conveniently and accurately. Considering the nonstationary property of the GHI on cloudy days, the GHI capturing is cast as an image regression problem. In traditional approaches, the image regression problem is treated as two parts, feature extraction and regression, which are optimized separately and no interconnections. Considering the nonlinear regression capability, a convolutional neural network (CNN) based image regression approach is proposed to provide an End-to- End solution for the cloudy day GHI capturing problem in this paper. For data cleaning, the Gaussian mixture model with Bayesian inference is employed to detect and eliminate the anomaly data in a nonparametric manner. The purified data are used as input data for the proposed image regression approach. The numerical results demonstrate the feasibility and effectiveness of the proposed approach.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.


Received from ProQuest

Rights holder

Yi Gu

File size

150 p.

File format





Electrical engineering