Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid

The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions

Simulation of ultrasonic vibration in a liquid aluminum bath for sapphire surface modification

Ultrasonic vibration has been found to play a significant role in promoting surface nano-crystallization of sapphire in a liquid aluminum bath. And the distribution of the vibration field is critical in controlling the modification procedure. Here, distribution of the ultrasonic vibration in a liquid aluminum bath was investigated by finite element method (FEM). Effects of shape of the ultrasonic horn and distance between the horn and the sapphire plates were investigated. It was found that the ultrasonic vibration density is high in the area adjacent to the ultrasonic horn. The distance between the horn and the plates significantly influence the vibration distribution. And the vibration density decreased significantly at the liquid/solid interface, indicating obvious energy absorption there. Vibration energy grads can be formed on sapphire surface. And this phenomenon shall be used to achieve different aims

The Impact of Different Herdsmen’s Size on Grassland Ecosystem of Eastern Transect in Tibetan Plateau: A Case Study from Maqu

In order to address grassland degradation, an individual contracting system, or namely, single-household rangeland management, based on Garrett Hardin theory of “tragedy of the commons”, had been conducted in Maqu since 1980s. Despite the support of local government, such a well-intentioned system saw little fruits on managing degradation, encountering extensive frustration due to its poor practical outcomes. Meanwhile, joint-household rangeland management, as an inheritance of locality-based management, has been vigorously pursued and has played an important role on Maqu’s grassland management system. In order to better illustrate the impact of size on managing the grassland ecosystem, an experiment was set up in which different herdsmen’s scales (single household, small-scale, medium-scale, large-scale, oversized-scale) existing in Maqu grassland was conducted. This study was an attempt to provide a groundbreaking management model, thus inspiring the policy-makers to achieve better solutions

Polycaprolactone/multi-walled carbon nanotube nerve guidance conduits with tunable channels fabricated via novel extrusion-stretching method for peripheral nerve repair

Multi-channeled nerve guidance conduit is a prospective way to repair peripheral nerve injury, which is still difficult to be fabricated. A novel extrusion-stretching method was utilized in this study to produce multi-walled carbon nanotubes (MWCNTs) loaded multi-channeled nerve conduits with improved flexibility and versatility. The channels and geography of the conduits were tunable. The results showed that the mechanical properties of the multi-channeled nerve conduits were suitable for peripheral nerve restoration. MWCNTs increased the biocompatibility of the multi-channeled nerve conduits. This study proved that the MWCNTs loaded multi-channeled produced by extrusion-stretching method have great potential to repair peripheral nerve injury

Case study on the effects of partial solar eclipse on distributed PV systems and management areas

Photovoltaic (PV) systems are weather-dependent. A solar eclipse causes significant changes in these parameters, thereby impacting PV generation profile, performance, and power quality of larger grid, where they connect to. This study presents a case study to evaluate the impacts of the solar eclipse of 21 August 2017, on two real-world grid-tied PV systems (1.4 MW and 355 kW) in Miami and Daytona, Florida, the feeders they are connected to, and the management areas they belong to. Four types of analyses are conducted to obtain a comprehensive picture of the impacts using 1 min PV generation data, hourly weather data, real feeder parameters, and daily reliability data. These analyses include: individual PV system performance measurement using power performance index; power quality analysis at the point of interconnection; a study on the operation of voltage regulating devices on the feeders during eclipse peak using an IEEE 8500 test case distribution feeder; and reliability study involving a multilayer perceptron framework for forecasting system reliability of the management areas. Results from this study provide a unique insight into how solar eclipses impact the behaviour of PV systems and the grid, which would be of concern to electric utilities in future high penetration scenarios

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization