Memory integrity protection has been a longstanding issue in trusted system design. Most viruses and malware attack the system by modifying data that they are not authorized to access. With the development of the Internet, viruses and malware spread much faster than ever before. In this setting, protecting the memory becomes increasingly important. However, it is a hard problem to protect the dynamic memory. The data in the memory changes from time to time so that the schemes have to be fast enough to provide real-time protection while in the same time the schemes have to use slow crytographical functions to keep the security level. In this thesis, we propose a new fast authentication scheme for memory. As in previous proposals the scheme uses a Merkle tree to guarantee dynamic protection of memory. We use the universal hash function family NH for speed and couple it with an AES encryption in order to achieve a high level of security. The proposed scheme is much faster compared to similar schemes achieved by cryptographic hash functions such as SHA-1 due to the finer grain incremental hashing ability provided by NH. With a modified version of the proposed scheme, the system can access the data in memory without checking the integrity all the time and still keeps the same security level. This feature is mainly due to the incremental nature of NH. Moreover, we show that combining with caches and parallelism, we can achieve fast and simple software implementation.

This thesis has proposed two types of real time System Integrity Protection Schemes(SIPS) using Emergency Single Machine Equivalent (E-SIME) and Model PredictiveControl (MPC) approaches respectively. They are aiming to resolve the transientstability problems in power systems. Synchronous measurements, fast communicationnetwork and FACTS are deployed in the two SIPSs. The Thyristor Controlled SeriesCompensation (TCSC) is applied as the control action in both SIPSs.In the E-SIME based SIPS, the SIME approach is used to evaluate the transient stabilityof the system and then a decision is made about the control actions needed to stabilizethe system. During emergency conditions, a fast response time is very important andthis requires a security guideline to be used in the decision making process. Theguideline is developed by analyzing offline multiple fault scenarios using an automaticlearning approach. This ensures appropriate control actions can be performed withoutcompromising the response time required on a real system.The MPC based SIPS optimizes the control action at every discrete time instant byselecting the control action that leads to the minimized cost function value. Automaticlearning (AL) is utilized to predict power system dynamics by assuming each controlaction has been taken. Furthermore, a feature selection technique, that chooses themost relevant variables, is used to improve the performance of the AL prediction. Themodel predictive control (MPC) technique is performed every discrete time interval, sothe optimal control action is always selected.Two types of SIPS are tested and verified in the benchmark systems. Simulation resultsshow they can effectively protect the system from loss of synchronism in the aftermathof a large disturbance. This thesis also compares the two SIPSs and concludes thebenefits and shortcomings of each approach.

The increasing volume of renewable and intermittent generation that is being connected to power systems means that system operators need more advanced dynamic control tools to manage the increase in congestion and the resulting pressure on system constraints. The introduction of synchronised measurement technology provides the wide area real-time measurements that are essential to develop and implement adaptive online solutions for current network issues. The objective of the research presented in this thesis is to design intelligent system integrity protection schemes (SIPS) that protect transmission lines and power transformers from thermal overloading. An intelligent protection scheme should be able to identify the fault severity, predict the post disturbance trend of system states, continue monitoring specific vulnerable system variables and propose an accurate solution that is tailored to the actual system conditions and the specific contingencies that have occurred. The intent of this research is to contribute to the development of adaptive protective schemes that are enabled by modern synchronized measurement technologies for future power systems. The research presented in this thesis focuses on the creation of novel Operational Tripping Schemes (OTSs) that explicitly satisfy both the functionality and economical requirements by integrating an improved assessment of thermal behaviour of the monitored assets. Novel OTSs are proposed for both transmission lines and transformers and they can be considered to be intelligent, adaptive and efficient SIPS for the thermal protection of system assets. A novel functional block is proposed that be included within the OTS and that uses optimization theory to determine the lowest cost solution to overheating in the time available. Furthermore, case studies have been conducted to verify the performance of each novel OTS using simulations of a full GB system model.

Recent major electrical disturbances highlight the extent to which modern societies depend on a reliable power infrastructure and the impact of these undesirable events on the economy and society. Numerous blackout models have been developed in the last decades that capture effectively the cascade mechanism leading to a partial or complete blackout. These models usually consider only the state of the electrical part of the system and investigate how failures or limitations in this system affect the probability and severity of a blackout.However, an analysis of the major disturbances that occurred during the last decade, such as the North America blackout of 2003 and the UCTE system disturbance of 2006, shows that failures or inadequacies in the Information and Communication Technology (ICT) infrastructure and also human errors had a significant impact on most of these blackouts.The aim of this thesis is to evaluate the contribution of these non-electrical events to the risk of power system blackouts. As the nature of these events is probabilistic and not deterministic, different probabilistic techniques have been developed to evaluate their impact on power systems reliability and operation.In particular, a method based on Monte Carlo simulation is proposed to assess the impact of an ICT failure on the operators’ situation awareness and consequently on their performance during an emergency. This thesis also describes a generic framework using Markov modeling for quantifying the impact of insufficient situation awareness on the probability of cascading electrical outages leading to a blackout. A procedure based on Markov modeling and fault tree analysis is also proposed for assessing the impact of ICT failures and human errors on the reliable operation of fast automatic protection actions, which are used to provide protection against fast-spreading electrical incidents. The impact of undesirable interactions and the uncoordinated operation of these protection schemes on power system reliability is also assessed in this thesis.The simulation results of these probabilistic methods show that a deterioration in the state of the ICT infrastructure and human errors affect significantly the probability and severity of power system blackouts. The conclusion of the work undertaken in this research is that failures in all the components of the power system, and not just the “heavy electrical” ones, must be considered when assessing the reliability of the electrical supply.