Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Spectral matrix methods for partitioning power grids: Applications to the Italian and Floridian high-voltage networks

Intentional islanding is used to limit cascading power failures by isolating highly connected "islands" with local generating capacity. To efficiently isolate an island, one should break as few power lines as possible. This is a graph partitioning problem, and here we give preliminary results on islanding of the Italian and Floridian high-voltage grids by spectral matrix methods.Comment: 4 pages, 2 figures

Application of Component Criticality Importance Measures in Design Scheme of Power Plants

This paper presents application of component criticality importance measures in phase of preparation and design of power plants. These measures provide a numerical rank to determine which components are more important for power plant reliability improvement or more critical for power plant failure. Identifying critical components for power plant reliability provides an important input for decision-making and guidance throughout the development project. The study on several schematic design options of conventional thermal power plant show that the importance measures can be used as an effective tool to assess component criticality in the project phase of new production capacities

New Approaches to Composite Reliability Assessment of Smart Power Systems

Electric power networks are complex systems because of their geographic spread and the consequent need for interconnections and integration of different components such as generators, transformers, lines, reactors, relays, and loads. Therefore, power utilities seek to ensure an acceptable degree of reliability in planning and operations, and accordingly, need information on component outages while satisfying the growing demand in order to ensure the availability of the system and prevent downtimes. Power systems of today are facing major challenges because of the rapid increase in penetration of energy resources (ERs) and plug-in electric vehicles (PEV). This thesis focuses on the evaluation of composite system reliability using direct probabilistic analysis techniques. The research presents the mathematical foundations, evaluation procedures, and reliability and risk indices associated with composite power system reliability evaluation using the minimal cut set calculations. The concept of minimal cut sets is applied to evaluate two sets of reliability and risk indices, system indices and nodal indices. System indices are essential for system planners and operators to determine the likelihood of interruption of supply, while nodal indices provide useful information on significant load points. The performance of the system under outage condition of generators, transmission lines, or both, is examined by conducting an appropriate power flow study. An optimal power flow (OPF) model is solved to find the system and nodal minimal cut sets and the associated indices. The thesis presents a novel composite system reliability based planning for ERs with clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and Fuzzy C-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an OPF based approach. Once the optimal sizes and locations of ERs are obtained, the earliest year of their penetration into the system is determined using an adequacy check algorithm. The thesis further presents a novel method to detect the critical components of composite power systems under steady-state conditions and short-term operations in order to help planners make economic decisions on new investments in generation capacities and transmission lines upgrades, and also to help operators maintain the delivery of electricity during system failure and disturbance events. Each component is ranked based on minimal cut set outage probability and the consequent loss of load arising from the outages of components belonging to a minimal cut set. Finally, the thesis presents a novel framework to evaluate the impact of PEV charging loads on composite power system reliability. A Smart-OPF model combined with a minimum cut set approach is proposed to evaluate the system reliability indices. Demand response (DR) is included in the proposed procedure and its impact on system reliability indices is studied. The procedure to determine the critical components of the power system in the presence of PEV loads and DR is also proposed