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.

Context: Cloud computing is rapidly emerging in the area of distributed computing. In the meantime, many organizations also attributed the technology to be associated with several business risks which are yet to be resolved. These challenges include lack of adequate security, privacy and legal issues, resource allocation, control over data, system integrity, risk assessment, software vulnerabilities and so on which all have compromising effect in cloud environment. Organizations based their worried on how to develop adequate mitigation strategies for effective control measures and to balancing common expectation between cloud providers and cloud users. However, many researches tend to focus on cloud computing adoption and implementation and with less attention to vulnerabilities and attacks in cloud computing. This paper gives an overview of common challenges and mitigation techniques or practices, describes general security issues and identifies future requirements for security research in cloud computing, given the current trend and industrial practices. Objectives: We identified common challenges and linked them with some compromising attributes in cloud as well as mitigation techniques and their impacts in cloud practices applicable in cloud computing. We also identified frameworks we consider relevant for identifying threats due to vulnerabilities based on information from the reviewed literatures and findings. Methods: We conducted a systematic literature review (SLR) specifically to identify empirical studies focus on challenges and mitigation techniques and to identify mitigation practices in addressing software vulnerabilities and attacks in cloud computing. Studies were selected based on the inclusion/exclusion criteria we defined in the SLR process. We search through four databases which include IEEE Xplore, ACM Digital Library, SpringerLinks and SciencDirect. We limited our search to papers published from 2001 to 2010. In additional, we then used the collected data and knowledge from finding after the SLR, to design a questionnaire which was used to conduct industrial survey which also identifies cloud computing challenges and mitigation practices persistent in industry settings. Results: Based on the SLR a total of 27 challenges and 20 mitigation techniques were identified. We further identified 7 frameworks we considered relevant for mitigating the prevalence real-time software vulnerabilities and attacks in the cloud. The identified challenges and mitigation practices were linked to compromised cloud attributes and the way mitigations practices affects cloud computing, respectively. Furthermore, 5 and 3 additional challenges and suggested mitigation practices were identified in the survey. Conclusion: This study has identified common challenges and mitigation techniques, as well as frameworks practices relevant for mitigating real-time software vulnerabilities and attacks in cloud computing. We cannot make claim on exhaustive identification of challenges and mitigation practices associated with cloud computing. We acknowledge the fact that our findings might not be sufficient to generalize the effect of the different service models which include SaaS, IaaS and PaaS, and also true for the different deployment models such as private, public, community and hybrid. However, this study we assist both cloud provider and cloud customers on the security, privacy, integrity and other related issues and useful in the part of identifying further research area that can help in enhancing security, privacy, resource allocation and maintain integrity in the cloud environment. / Kungsmarksvagen 67 SE-371 44 Karlskrona Sweden Tel: 0737159290

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.