Making Networks Robust to Component Failures

In this thesis, we consider instances of component failure in the Internet and in networked cyber-physical systems, such as the communication network used by the modern electric power grid (termed the smart grid). We design algorithms that make these networks more robust to various component failures, including failed routers, failures of links connecting routers, and failed sensors. This thesis divides into three parts: recovery from malicious or misconfigured nodes injecting false information into a distributed system (e.g., the Internet), placing smart grid sensors to provide measurement error detection, and fast recovery from link failures in a smart grid communication network. First, we consider the problem of malicious or misconfigured nodes that inject and spread incorrect state throughout a distributed system. Such false state can degrade the performance of a distributed system or render it unusable. For example, in the case of network routing algorithms, false state corresponding to a node incorrectly declaring a cost of 0 to all destinations (maliciously or due to misconfiguration) can quickly spread through the network. This causes other nodes to (incorrectly) route via the misconfigured node, resulting in suboptimal routing and network congestion. We propose three algorithms for efficient recovery in such scenarios and evaluate their efficacy. The last two parts of this thesis consider robustness in the context of the electric power grid. We study the use and placement of a sensor, called a Phasor Measurement Unit (PMU), currently being deployed in electric power grids worldwide. PMUs provide voltage and current measurements at a sampling rate orders of magnitude higher than the status quo. As a result, PMUs can both drastically improve existing power grid operations and enable an entirely new set of applications, such as the reliable integration of renewable energy resources. However, PMU applications require correct (addressed in thesis part 2) and timely(covered in thesis part 3) PMU data. Without these guarantees, smart grid operators and applications may make incorrect decisions and take corresponding (incorrect) actions. The second part of this thesis addresses PMU measurement errors, which have been observed in practice. We formulate a set of PMU placement problems that aim to satisfy two constraints: place PMUs near each other to allow for measurement error detection and use the minimal number of PMUs to infer the state of the maximum number of system buses and transmission lines. For each PMU placement problem, we prove it is NP-Complete, propose a simple greedy approximation algorithm, and evaluate our greedy solutions. In the last part of this thesis, we design algorithms for fast recovery from link failures in a smart grid communication network. We propose, design, and evaluate solutions to all three aspects of link failure recovery: (a) link failure detection, (b) algorithms for pre-computing backup multicast trees, and (c) fast backup tree installation. To address (a), we design link-failure detection and reporting mechanisms that use OpenFlow to detect link failures when and where they occur inside the network. OpenFlow is an open source framework that cleanly separates the control and data planes for use in network management and control. For part (b), we formulate a new problem, Multicast Recycling, that pre-computes backup multicast trees that aim to minimize control plane signaling overhead. We prove Multicast Recycling is at least NP-hard and present a corresponding approximation algorithm. Lastly, two control plane algorithms are proposed that signal data plane switches to install pre-computed backup trees. An optimized version of each installation algorithm is designed that finds a near minimum set of forwarding rules by sharing forwarding rules across multicast groups. This optimization reduces backup tree install time and associated control state. We implement these algorithms using the POX open-source OpenFlow controller and evaluate them using the Mininet emulator, quantifying control plane signaling and installation time

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Impact Assessment, Detection, and Mitigation of False Data Attacks in Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Protection Challenges of Distributed Energy Resources Integration In Power Systems

It is a century that electrical power system are the main source of energy for the societies and industries. Most parts of these infrastructures are built long time ago. There are plenty of high rating high voltage equipment which are designed and manufactured in mid-20th and are currently operating in United States’ power network. These assets are capable to do what they are doing now. However, the issue rises with the recent trend, i.e. DERs integration, causing fundamental changes in electrical power systems and violating traditional network design basis in various ways. Recently, there have been a steep rise in demands for Distributed Energy Resources (DERs) integration. There are various incentives for demand in such integrations and employment of distributed and renewable energy resources. However, it violates the most fundamental assumption in power system traditional designs. That is the power flows from the generation (upstream) toward the load locations (downstream). Currently operating power systems are designed based on this assumption and consequently their equipment ratings, operational details, protection schemes, and protections settings. Violating these designs and operational settings leads toward reducing the power reliability and increasing outages, which are opposite of the DERs integration goals. The DERs integration and its consequences happen in both transmission and distribution levels. Both of these networks effects of DERs integration are discussed in this dissertation. The transmission level issues are explained in brief and more analytical approach while the transmission network challenges are provided in details using both field data and simulation results. It is worth mentioning that DERs integration is aligned with the goal to lead toward a smart grid. This can be considered the most fundamental network reconfiguration that has ever experienced and requires various preparations. Both long term and short term solutions are proposed for the explained challenges and corresponding results are provided to illustrate the effectiveness of the proposed solutions. The author believes that developing and considering short term solutions can make the transition period toward reaching the smart grid possible. Meanwhile, long term approaches should also be planned for the final smart grid development and operation details

Wide-Area Control Schemes to Improve Small Signal Stability in Power Systems

One of the main concerns for the secure and reliable operation of power systems is the small signal stability problem. In the complex and highly interconnected structure of future power systems, relying solely on operator responses and conventional controls cannot assure reliability. Therefore, there is a need for advanced Wide-Area Control Schemes (WACS) that can automatically respond to degradation of reliability in the system. The main objective of this dissertation is to address two key challenges regarding the design and implementation of wide-area control schemes for damping inter-area oscillations. First is the high communication cost associated with optimal centralized control approaches. As power networks are large-scale systems, both the synthesis and the implementation of centralized controllers suggested by most of the previous studies are often impossible in practice. Second is the difficulty of obtaining accurate system-wide dynamic models for initiating and updating the control design. In this research, we introduced wide-area damping control strategies that not only ensure the small signal stability with the desired performance but also consider communication and model information limitations in the design. A state feedback formulation is proposed that aims to simultaneously optimize a standard Linear Quadratic Regulator (LQR) cost criterion and induce a pre-defined communication structure. We solved the proposed problem with three different objectives to target a specific wide-area damping control design challenge in each setting. First, the communication structure is enforced as a constraint in the optimization and solved for a large idealized power network with information symmetry. Second, to make the method suitable for systems with arbitrary structures and information patterns, we proposed a group-sparse regularization to be added to the optimization cost function. Applications of the method for inducing the desired communication network and finding effective measurement and control signal combinations were also investigated. Third, we paired the proposed optimal control with a real-time model identification approach, to create a wide-area control framework that is capable of dealing with model information limitations and inaccuracies in online implementation. The performances of the proposed wide-area damping control architectures are validated through nonlinear simulations on different test systems

On Statistical QoS Provisioning for Smart Grid

Current power system is in the transition from traditional power grid to Smart Grid. A key advantage of Smart Grid is its integration of advanced communication technologies, which can provide real-time system-wide two-way information links. Since the communication system and power system are deeply coupled within the Smart Grid system, it makes Quality of Service (QoS) performance analysis much more complex than that in either system alone. In order to address this challenge, the effective rate theory is studied and extended in this thesis, where a new H transform based framework is proposed. Various scenarios are investigated using the new proposed effective rate framework, including both independent and correlated fading channels. With the effective rate as a connection between the communication system and the power system, an analysis of the power grid observability under communication constraints is performed. Case studies show that the effective rate provides a cross layer analytical framework within the communication system, while its statistical characterisation of the communication delay has the potential to be applied as a general coupling point between the communication system and the power system, especially when real-time applications are considered. Besides the theoretical QoS performance analysis within Smart Grid, a new Software Defined Smart Grid testbed is proposed in this thesis. This testbed provides a versatile evaluation and development environment for Smart Grid QoS performance studies. It exploits the Real Time Digital Simulator (RTDS) to emulate different power grid configurations and the Software Defined Radio (SDR) environment to implement the communication system. A data acquisition and actuator module is developed, which provides an emulation of various Intelligent Electronic Devices (IEDs). The implemented prototype demonstrates that the proposed testbed has the potential to evaluate real time Smart Grid applications such as real time voltage stability control

An Information-Centric Communication Infrastructure for Real-Time State Estimation of Active Distribution Networks

© 2010-2012 IEEE.The evolution toward emerging active distribution networks (ADNs) can be realized via a real-time state estimation (RTSE) application facilitated by the use of phasor measurement units (PMUs). A critical challenge in deploying PMU-based RTSE applications at large scale is the lack of a scalable and flexible communication infrastructure for the timely (i.e., sub-second) delivery of the high volume of synchronized and continuous synchrophasor measurements. We address this challenge by introducing a communication platform called C-DAX based on the information-centric networking (ICN) concept. With a topic-based publish-subscribe engine that decouples data producers and consumers in time and space, C-DAX enables efficient synchrophasor measurement delivery, as well as flexible and scalable (re)configuration of PMU data communication for seamless full observability of power conditions in complex and dynamic scenarios. Based on the derived set of requirements for supporting PMU-based RTSE in ADNs, we design the ICN-based C-DAX communication platform, together with a joint optimized physical network resource provisioning strategy, in order to enable the agile PMU data communications in near real-time. In this paper, C-DAX is validated via a field trial implementation deployed over a sample feeder in a real-distribution network; it is also evaluated through simulation-based experiments using a large set of real medium voltage grid topologies currently operating live in The Netherlands. This is the first work that applies emerging communication paradigms, such as ICN, to smart grids while maintaining the required hard real-time data delivery as demonstrated through field trials at national scale. As such, it aims to become a blueprint for the application of ICN-based general purpose communication platforms to ADNs