Graphical Convolution Network Based Semi-Supervised Methods for Detecting PMU Data Manipulation Attacks

With the integration of information and communications technologies (ICTs) into the power grid, electricity infrastructures are gradually transformed towards smart grid and power systems become more open to and accessible from outside networks. With ubiquitous sensors, computers and communication networks, modern power systems have become complicated cyber-physical systems. The cyber security issues and the impact of potential attacks on the smart grid have become an important issue. Among these attacks, false data injection attack (FDIA) becomes a growing concern because of its varied types and impacts. Several detection algorithms have been developed in the last few years, which were model-based, trajectory prediction-based or learning-based methods. Phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) system work together to monitor the power system operation. The unsecured devices could offer opportunities to adversaries to compromise the system. In the literature review part of this thesis, the main methods are compared considering computing accuracy and complexity. Most work about PMUs ignored the reality that the number of PMUs installed in a power system is limited to realize observability because of high installing cost. Therefore, based on observable truth of PMU and the topology structure of power system, the graph convolution network (GCN) is proposed in this thesis. The main idea is using selected features to define violated PMU, and GCN is used to classify susceptible violated nodes and normal nodes. The basic detection method is introduced at first. And then the calculation process of neural network and Fourier transform are described with more details about graph convolution network. Later, the proposed detection mechanism and algorithm are introduced. Finally, the simulation results are given and analyzed

Achieving High Renewable Energy Integration in Smart Grids with Machine Learning

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest. In recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids. The research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid. The objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation. The research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids. Simulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix

Achieving High Renewable Energy Integration in Smart Grids with Machine Learning

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest. In recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids. The research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid. The objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation. The research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids. Simulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix