Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Transmission Expansion Planning : computational challenges toward real-size networks

The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system. Due to increasing sources of uncertainty such as more intermittent energy resources, mobile and controllable demands, and fast technology improvements for PVs and energy storage devices, the need for using systematic ways for solving this complex problem is increased. One of the main barriers for deploying optimization-based TEP studies is computationally intractability, which is the main motivation for this research. The aim of this work is to investigate the computational challenges associated with systematic TEP studies for large-scale problems, and develop algorithms to improve computational performance. In the first step, we investigate the impact of adding security constraints (as NERC standard requirement) into TEP optimization problem, and develop the Variable Contingency List (VCL) algorithm to pre-screen security constraints to only add those that may affect the feasible region. It significantly decreases the size of the problem compared to considering all security constraints. Then, we evaluate the impact of the size of candidate lines list (number of binary variables) on TEP, and developed a heuristic algorithm to decrease the size of this list. In the next step, we integrate uncertainties into the TEP optimization problem and formulate the problem as a two-stage stochastic program. Adding uncertainties increases the size of the problem significantly. It leads us to develop a three-level filter that introduces important scenario identification index (ISII) and similar scenario elimination (SSE) technique to decrease the number of security constraints in stochastic TEP in a systematic and tractable way. We then investigate the scalability of the stochastic TEP formulation. We develop a configurable decomposition framework that allows us to decompose the original problem into subproblems that can be solved independently and in parallel. This framework can benefit from using both progressive hedging (PH) and Benders decomposition (BD) algorithms to decompose and parallelize a large-scale problem both vertically and horizontally. We have also developed a bundling algorithm that improves the performance of PH algorithm and the overall performance of the framework. We have implemented our work on a reduced ERCOT network with more than 3000 buses to demonstrate the practicality of the proposed method in this work for large-scale problems.Electrical and Computer Engineerin

Electrical Infrastructure Adaptation for a Changing Climate

In recent years, global climate change has become a major factor in long-term electrical infrastructure planning in coastal areas. Over time, accelerated sea level rise and fiercer, more frequent storm surges caused by the changing climate have imposed increasing risks to the security and reliability of coastal electrical infrastructure systems. It is important to ensure that infrastructure system planning adapts to such risks to produce systems with strong resilience. This dissertation proposes a decision framework for long-term, resilient electrical infrastructure adaptation planning for a future with the uncertain sea level rise and storm surges in a changing climate. As uncertainty is unavoidable in real-world decision making, stochastic optimization plays an essential role in making robust decisions with respect to global climate change. The core of the proposed decision framework is a stochastic optimization model with the primary goal being to ensure operational feasibility once uncertain futures are revealed. The proposed stochastic model produces long-term climate adaptations that are subject to both the exogenous uncertainty of climate change as well as the endogenous physical restrictions of electrical infrastructure. Complex, state-of-the-art simulation models under climate change are utilized to represent exogenous uncertainty in the decision-making process. In practice, deterministic methods such as scenario-based analyses and/or geometric-information-system-based heuristics are widely used for real-world adaptation planning. Numerical experiments and sensitivity analyses are conducted to compare the proposed framework with various deterministic methods. Our experimental results demonstrate that resilient, long-term adaptations can be obtained using the proposed stochastic optimization model. In further developing the decision framework, we address a class of stochastic optimization models where operational feasibility is ensured for only a percentage of all possible uncertainty realizations through joint chance-constraints. It is important to identify the significant scalability limitations often associated with commercial optimization tools for solving this class of challenging stochastic optimization problems. We propose a novel configuration generation algorithm which leverages metaheuristics to find high-quality solutions quickly and generic relaxations to provide solution quality guarantees. A key advantage of the proposed method over previous work is that the joint chance-constrained stochastic optimization problem can contain multivariate distributions, discrete variables, and nonconvex constraints. The effectiveness of the proposed algorithm is demonstrated on two applications, including the climate adaptation problem, where it significantly outperforms commercial optimization tools. Furthermore, the need to address the feasibility of a realistic electrical infrastructure system under impacts is recognized for the proposed decision framework. This requires dedicated attention to addressing nonlinear, nonconvex optimization problem feasibility, which can be a challenging problem that requires an expansive exploitation of the solution space. We propose a global algorithm for the feasibility problem\u27s counterpart: proving problem infeasibility. The proposed algorithm adaptively discretizes variable domains to tighten the relaxed problem for proving infeasibility. The convergence of the algorithm is demonstrated as the algorithm either finds a feasible solution or terminates with the problem being proven infeasible. The efficiency of this algorithm is demonstrated through experiments comparing two state-of-the-art global solvers, as well as a recently proposed global algorithm, to our proposed method

Probabilistic Optimization Techniques in Smart Power System

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.publishedVersio

Reliability and Maintenance

Amid a plethora of challenges, technological advances in science and engineering are inadvertently affecting an increased spectrum of today’s modern life. Yet for all supplied products and services provided, robustness of processes, methods, and techniques is regarded as a major player in promoting safety. This book on systems reliability, which equally includes maintenance-related policies, presents fundamental reliability concepts that are applied in a number of industrial cases. Furthermore, to alleviate potential cost and time-specific bottlenecks, software engineering and systems engineering incorporate approximation models, also referred to as meta-processes, or surrogate models to reproduce a predefined set of problems aimed at enhancing safety, while minimizing detrimental outcomes to society and the environment