Electrical power systems are vulnerable to contingencies due to the possibility of fault or damage. When a serious fault (which cannot be removed immediately) occurs in the power system, the fault needs to be isolated, and the remaining system should be reconfigured to make efficient use of available energy. Reconfiguring the system by making an island can be one of the strategies to continue the energy supply using the remaining generation capacity. By analyzing the effect of contingencies on power systems in terms of voltage support, reliability and stability, survivability of the system can be predicted. A survivability index of the system can be developed based on voltage support, reliability and the stability index with predefined weight given to each performance criterion. These weights may vary for different system conditions. The purpose of this research work is to develop a survivability index for shipboard power system and rank reconfiguration choices based on the developed index.

For service providers, extending the Software Defined Network (SDN) concept from packet switching in Layers 2 and 3 to circuit switching in transport layers is a promising approach to meet high burstiness and high bandwidth requirements. A multi-layer controller that can provide automated controller-based restoration and protection, even for unprotected links in a multi-administrative domain, would be a significant improvement. It would allow service providers to ensure provision of guaranteed Service Level Agreement (SLA) maintenance, with optimal bandwidth usage, high availability and reduced errors. In this thesis, we propose a Software Defined Survivable Optical Interconnect (SDSOI) architecture for Data Centers (DC). This unique architecture will address service providers’ challenges related to bandwidth management, and optimize the time required while interconnecting numerous DCs to meet the high SLA demands. The architecture is built according to the overlay SDN concept, and categorizes the application layers into online, offline and third party applications. The offline application performs the routine DC tasks, while the online application manages various dynamic DC demands. An SDSOI driven Data Center Optical Interconnect (DCOI) can handle the extensive, high quality, on-demand access to the contents. The feasibility of SDSOI is verified and demonstrated using Open Network Operating System (ONOS) as the SDN controller, Mininet as the network emulator and Optical Transport Network (OTN) as the optical framework. Our work primarily focusses on the creation of business applications in the SDSOI model, and the northbound protocols or interfaces used by the applications to interact with the controller. To verify the effectiveness of the proposed SDSOI architecture and its business applications, we simulated ‘Day Night Scheduling’, an application that combines characteristics of offline and online applications. Its primary function is to package and schedule varying DC bandwidths to service providers and optimize bandwidth usage at different times of day, along with the survivability of the interconnects.

In this thesis, I proposed a novel framework of all-optical failure restoration which jointly determines network monitoring plane and spare capacity allocation in the presence of either static or dynamic traffic. The proposed framework aims to enable a general shared protection scheme to achieve near optimal capacity efficiency as in Failure Dependent Protection(FDP) while subject to an ultra-fast, all-optical, and deterministic failure restoration process. Simply put, Local Unambiguous Failure Localization(L-UFL) and FDP are the two building blocks for the proposed restoration framework. Under L-UFL, by properly allocating a set of Monitoring Trails (m-trails), a set of nodes can unambiguously identify every possible Shared Risk Link Group (SRLG) failure merely based on its locally collected Loss of Light(LOL) signals. Two heuristics are proposed to solve L-UFL, one of which exclusively deploys Supervisory Lightpaths (S-LPs) while the other jointly considers S-LPs and Working Lightpaths (W-LPs) for suppressing monitoring resource consumption. Thanks to the ``Enhanced Min Wavelength Max Information principle'', an entropy based utility function, m-trail global-sharing and other techniques, the proposed heuristics exhibit satisfactory performance in minimizing the number of m-trails, Wavelength Channel(WL) consumption and the running time of the algorithm. Based on the heuristics for L-UFL, two algorithms, namely MPJD and DJH, are proposed for the novel signaling-free restoration framework to deal with static and dynamic traffic respectively. MPJD is developed to determine the Protection Lightpaths (P-LPs) and m-trails given the pre-computed W-LPs while DJH jointly implements a generic dynamic survivable routing scheme based on FDP with an m-trail deployment scheme. For both algorithms, m-trail deployment is guided by the Necessary Monitoring Requirement (NMR) defined at each node for achieving signaling-free restoration. Extensive simulation is conducted to verify the performance of the proposed heuristics in terms of WL consumption, number of m-trails, monitoring requirement, blocking probability and running time. In conclusion, the proposed restoration framework can achieve all-optical and signaling-free restoration with the help of L-UFL, while maintaining high capacity efficiency as in FDP based survivable routing. The proposed heuristics achieve satisfactory performance as verified by the simulation results.

In this thesis, I proposed a novel framework of all-optical failure restoration which jointly determines network monitoring plane and spare capacity allocation in the presence of either static or dynamic traffic. The proposed framework aims to enable a general shared protection scheme to achieve near optimal capacity efficiency as in Failure Dependent Protection(FDP) while subject to an ultra-fast, all-optical, and deterministic failure restoration process. Simply put, Local Unambiguous Failure Localization(L-UFL) and FDP are the two building blocks for the proposed restoration framework. Under L-UFL, by properly allocating a set of Monitoring Trails (m-trails), a set of nodes can unambiguously identify every possible Shared Risk Link Group (SRLG) failure merely based on its locally collected Loss of Light(LOL) signals. Two heuristics are proposed to solve L-UFL, one of which exclusively deploys Supervisory Lightpaths (S-LPs) while the other jointly considers S-LPs and Working Lightpaths (W-LPs) for suppressing monitoring resource consumption. Thanks to the ``Enhanced Min Wavelength Max Information principle'', an entropy based utility function, m-trail global-sharing and other techniques, the proposed heuristics exhibit satisfactory performance in minimizing the number of m-trails, Wavelength Channel(WL) consumption and the running time of the algorithm. Based on the heuristics for L-UFL, two algorithms, namely MPJD and DJH, are proposed for the novel signaling-free restoration framework to deal with static and dynamic traffic respectively. MPJD is developed to determine the Protection Lightpaths (P-LPs) and m-trails given the pre-computed W-LPs while DJH jointly implements a generic dynamic survivable routing scheme based on FDP with an m-trail deployment scheme. For both algorithms, m-trail deployment is guided by the Necessary Monitoring Requirement (NMR) defined at each node for achieving signaling-free restoration. Extensive simulation is conducted to verify the performance of the proposed heuristics in terms of WL consumption, number of m-trails, monitoring requirement, blocking probability and running time. In conclusion, the proposed restoration framework can achieve all-optical and signaling-free restoration with the help of L-UFL, while maintaining high capacity efficiency as in FDP based survivable routing. The proposed heuristics achieve satisfactory performance as verified by the simulation results.

Network design problems involving survivability usually include trade-off of the potential for lost revenues and customer goodwill against the extra costs required to increase the network survivability. It also involves selection of nodes and edges from lists of potential sets to accomplish certain desirable properties. In many applications it is imperative to have built-in reliability or survivability of the network. Delays of traffic are undesirable since it affects quality of service (QoS) to clients of the network. In this dissertation we consider the construction of an optimization system for network design with survivability properties that may help in the planning of mesh topologies while maintaining a certain degree of survivability of the network. This is done by providing for at least two diverse paths between certain "special" nodes to provide protection against any single edge or node failure. This part is modelled by using mixed integer programming techniques. A software product called CPLEX then solves these models and various facilities are built into the decision support system to allow the decision maker to experiment with some topological and flow requirement changes. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007.

In shipboard power systems (SPS), it is important to provide continuous power to vital loads so that their desired missions can be completed successfully. Several components exist between the primary source and the vital load such as transformers, cables, or switching devices. These components can fail due to mechanical stresses, electrical stresses, and overloading which could lead to a system failure. If the normal path to a vital load cannot supply power to it, then it should be powered through its alternate path. The process of restoring, balancing, and minimizing power losses to loads is called network reconfiguration. Prognostics is the ability to predict precisely and accurately the remaining useful life of a failing component. In this work, the prognostic information of the power system components is used to determine if reconfiguration should be performed if the system is unable to accomplish its mission. Each component will be analyzed using the Weibull Distribution to compute the conditional reliability from present time to the end of the mission. To determine if reconfiguration is needed, all components to a given load will be utilized in structure functions to determine if a load will be able to survive during a time period. Structure functions are used to show how components are interconnected, and also provide a mathematical means for computing the total probability of a system. This work will provide a method to compute the conditional survivability to a given load, and the results indicate the top five loads that have the lowest conditional survivability during a mission in known configuration. The results show the computed conditional survivability of loads on an all electric navy ship. The loads conditional survivability is computed on high/medium voltage level and a low voltage level to show how loads are affected by failing components along their path.

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Network design problems involving survivability usually include trade-off of the potential for lost revenues and customer goodwill against the extra costs required to increase the network survivability. It also involves selection of nodes and edges from lists of potential sets to accomplish certain desirable properties. In many applications it is imperative to have built-in reliability or survivability of the network. Delays of traffic are undesirable since it affects quality of service (QoS) to clients of the network. In this dissertation we consider the construction of an optimization system for network design with survivability properties that may help in the planning of mesh topologies while maintaining a certain degree of survivability of the network. This is done by providing for at least two diverse paths between certain "special" nodes to provide protection against any single edge or node failure. This part is modelled by using mixed integer programming techniques. A software product called CPLEX then solves these models and various facilities are built into the decision support system to allow the decision maker to experiment with some topological and flow requirement changes. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007

A highly survivable communication network is desirable, as service disruption is usually not tolerated. In this thesis we mainly investigate and discuss the survivability of two specific communication networks, termed topology 1 and 2, under usual network failures. The survivability of the networks mainly comes from their structure. Both topologies have different routes between all source-destination pairs, which gives the networks high route diversity. In fact, both topologies considered are regular networks with connectivity 3. Discussion starts by defining several network properties, such as average route-length and link and node utilization, for the fault-free condition. Alterations of these properties are investigated when a network failure takes place. Using the results for fault-free and faulty situations, a comparison is made between the topologies. Topologies 1 and 2 are also compared with other standard topologies like full ring, square grid and star topologies. Another regular network topology called the star-ring topology is also introduced and investigated for the same properties. Enough insight is given to devise an optimal re-routing strategy when a network failure takes place. A new idea of static routing strategy called the Static Disjoint Routing Strategy is introduced. This disjoint routing strategy is proven to be close in performance to that of traditional Dynamic Shortest Routing with a considerable gain in ease of operation. The disjoint routing table is used to investigate whether any link or node becomes over utilized in faulty situations. On the whole both topologies were found to be highly survivable structures with reasonable cost. / Master of Science