A Closed-Form Technique for the Reliability and Risk Assessment of Wind Turbine Systems

This paper proposes a closed-form method to evaluate wind turbine system reliability and associated failure consequences. Monte Carlo simulation, a widely used approach for system reliability assessment, usually requires large numbers of computational experiments, while existing analytical methods are limited to simple system event configurations with a focus on average values of reliability metrics. By analyzing a wind turbine system and its components in a combinatorial yet computationally efficient form, the proposed approach provides an entire probability distribution of system failure that contains all possible configurations of component failure and survival events. The approach is also capable of handling unique component attributes such as downtime and repair cost needed for risk estimations, and enables sensitivity analysis for quantifying the criticality of individual components to wind turbine system reliability. Applications of the technique are illustrated by assessing the reliability of a 12-subassembly turbine system. In addition, component downtimes and repair costs of components are embedded in the formulation to compute expected annual wind turbine unavailability and repair cost probabilities, and component importance metrics useful for maintenance planning and research prioritization. Furthermore, this paper introduces a recursive solution to closed-form method and applies this to a 45-component turbine system. The proposed approach proves to be computationally efficient and yields vital reliability information that could be readily used by wind farm stakeholders for decision making and risk management

Database-assisted design methodology to predict wind-induced structural behavior of a light-framed wood building

This study investigates the applicability of the database-assisted design (DAD) methodology to predict structural reactions in a light-framed wood structure subjected to fluctuating wind pressures. Structural influence functions were determined on a 1/3-scale light-frame wood structure, which was then subjected to a wind flow, while the surface pressures and structural reactions at roof-to-wall and wall-to-foundation connections were simultaneously recorded. There was a good agreement between the DAD-predicted structural reactions and experimentally measured reactions, confirming that the DAD method is suitable for predicting the structural reactions in light-frame wood buildings. Subsequently structural reaction time histories at several connections within the building were generated using a 1:50 scale wind tunnel model of the structure and the peak structural reactions determined using the DAD method and previously obtained influence functions. When the DAD-estimated reactions were compared with reactions predicted by the ASCE 7-05 main wind force resisting system (MWFRS) method, they showed the ASCE 7 reactions were highly non-conservative(i.e. smaller than the DAD method predictions), by as much as 39% at the gable end truss. The components and cladding method showed reasonable agreement with the DAD method for the gable end and first interior truss reactions but it too underestimated the reaction loads at the second and third interior trusses by 30% and 12% respectively

A data fusion probabilistic model for hurricane-induced outages in electric power grids

Prediction of outages in electric power systems before a hurricane can be enhanced by exploiting data not typically used for such purposes, including system contingencies during normal operation. This data-based enhancement is necessary to inform disaster planning and preparedness, as well as to speed up the restoration of the system while capturing local trends. This paper presents a framework that integrates hurricane-induced outage predictions based on component fragilities, physics of power flows, and network responses, along with increasingly available spatio-temporal information of daily outages, so as to better assess outage risks in the electric system. The study adapts Linearly Constrained Least Squares (LCLS) by making space the dependent variable instead of time (as traditionally used), and then determine an optimal linear fusion of the predicted outages with information from the daily outage trackers for enhanced outage distribution assessment. Using the electric power system as an illustrative example, the study shows how data fusion improves the prediction accuracy by including daily outage information, which implicitly contains the spatial structure of the network as well as the current physical state of its components and interactions, including ageing, fatigue and recent hardening or embedding of smart grid technologies. The fusion-based framework predicts an overall outage of 84% in the county under Hurricane Ike winds, which strongly agrees with the reported outage of 86% by the utility provider in the aftermath of the event as opposed to overall outage of 90% predicted without data fusion.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyResearche